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104-118 

104 

 

 

 

Article 

Cascade CNN: a two-stage segmentation framework 

for efficient and accurate brain tumor 

segmentation in multi-modal MRI 
M. Vamsikrishna1,2*, Chin-Shiuh Shieh3 

1Research Scholar, Research Institute of IoT Cybersecurity, Department of Electronic Engineering, National Kaohsiung 

University of Science and Technology, Taiwan 
2Department of Computer Applications, Aditya University, Surampalem, Kakinada, Andhra Pradesh, India 
3National Kaohsiung University of Science and Technology, Taiwan 

A R T I C L E   I N F O 
 

Article history: 
Received 18 March 2025  
Received in revised form 
30 April 2025 
Accepted 10 May 2025 
 
Keywords:  
Cascade CNN, Multi-modal MRI, Medical image 
analysis, CoarseNet, RefineNet 
 
*Corresponding author 
Email address: 
vkmangalampalli@gmail.com 
 
 
DOI: 10.55670/fpll.futech.4.2.10 

A B S T R A C T 
 

The region of a brain tumor is critical in gliomas diagnosis and treatment, which 
involves multi-modal MRI segmentation. While segmentation models like U-Net 
and nnU-Net do exist, they aren't effective in dealing with small tumor 
structures or with limited computational resources in general. To address these 
drawbacks, we propose a Cascade CNN (C-CNN) Model. C-CNN is a two-stage 
model that consists of two processes: coarse segmentation and refined 
segmentation. CoarseNet is the first process roughly segments the tumor and 
localizes the Region of Interest (ROI). This is succeeded by RefineNet, which 
does thorough multi-class segmentation on the cropped ROI, dividing the image 
into edema, Whole Tumor(WT), tumor core (TC), and enhancing tumor (ET). 
Our sequential training and multi-modal (T1, T1ce, T2, FLAIR) MRI inputs to the 
model reduce false positives and improve segmentation accuracy. We 
implemented our approach on the BraTS 2023 dataset and achieved the 
following Dice scores: 89.1% for WT, 83.2% for TC, 78.3% for ET, which bested 
single-stage models' results. Adaptive cropping further allows for lower 
computational costs, enabling the algorithm to be implemented in real-time 
clinical settings. 

1. Introduction 

Gliomas are one of the most heterogeneous and 

aggressive types of brain tumors, which makes accurate 

volume estimation of tumor subregions critical for effective 

treatment planning [1]. Segmentation encompasses the 

delineation of an MRI image into whole tumor (WT), tumor 

core (TC), and enhancing tumor (ET), all of which are 

important for planning radiotherapy and surgery [2]. U-Net, 

nnU-Net, and other models built on deep learning have shown 

great segmentation results, even though they are very 

complex computationally and do not segment smaller tumor 

structures accurately [3,4]. The tasks of segmentation of 

tumors are well developed in deep learning models that 

utilize CNN, vision transformers, and even hybrid CNN-

transformer architectures. For example, U-Net modifications 

based on convolutional neural networks achieved Dice scores 

close to 0.90 in whole tumor segmentation tasks [5]. At the 

same time, architecture that utilizes Transformers, such as 

Swin-UNETR and TransBTS, also performed better because 

they are able to look at long-range dependencies [6,7]. Almost 

all of these models are noted for being computationally 

expensive, very sensitive to small samples, and prone to 

overfitting [8]. Newer hybrid models that use CNNs and 

Transformers have been able to outperform others by 

offering a better trade-off between local context feature 

extraction and global context mapping [9]. Despite these 

advancements, deep learning methods still struggle with 

ambiguous tumor boundaries, false positives, and 

computational inefficiencies in real-time clinical applications 

[10]. This paper presents Cascade CNN (C-CNN), a novel two-

stage segmentation framework that has been designed to 

address the challenges mentioned above. Rather than existing 

single-stage models or former cascade strategies, C-CNN uses 

a coarse pass segmentation technique, which aims at 

maximizing the recall of the tumor, and a refined 

segmentation stage, which aims at the precision of the 

delineation of the tumor subregions. The main advantage of 

our proposed approach is the combination of adaptive ROI 

cropping and sequential multimodal training.  

 

 

 

Future Technology 

Open Access Journal 

https://doi.org/10.55670/fpll.futech.4.2.10 

May 2025| Volume 04 | Issue 02 | Pages 104 - 118 

Journal homepage: https://fupubco.com/futech 

 

ISSN 2832-0379 

mailto:vkmangalampalli@gmail.com
https://doi.org/10.55670/fpll.futech.4.2.10
https://fupubco.com/futech


M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

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By cropping the ROI, the relevant area is focused on, 

while computation is reduced. Moreover, the model is trained 

using modalities T1, T1ce, T2, and FLAIR MRI sequences. Our 

approach is able to reduce false positives and capture small 

or faint tumor structures by first isolating the tumor region 

and then refining it. This allows us to increase accuracy and 

always obtain the desired output while eliminating the need 

for setting complicated parameters. The engineered cascade 

design enables us to directly address the problems of class 

imbalance and blurry boundaries. In conclusion, C-CNN is 

more effective and efficient than other models when it comes 

to brain tumor segmentation, which we show with 

experiments we ran on the BraTS 2023 dataset. The rest of 

the paper describes the background on related work (Section 

2), a detailed description of the proposed methodology 

(Section 3), results and comparisons (Section 4), and the 

conclusions and future works. 

2. Related work 

Brain tumor segmentation has significantly benefited 

from deep learning, especially with the evolution of encoder-

decoder architectures such as U-Net and its derivatives. U-

Net, introduced by Ronneberger et al. [3], is a foundational 

model in biomedical segmentation, using skip connections to 

integrate semantic and spatial features. However, its ability to 

capture fine-grained tumor boundaries, especially for small 

subregions, is limited [11, 12]. To improve over U-Net, nnU-

Net was proposed as a self-configuring framework that 

adapts its architecture to the dataset specifics. It has 

consistently achieved high performance across medical 

segmentation tasks, including brain tumors, with reported 

Dice scores around 0.89 (WT), 0.81 (TC), and 0.78 (ET) [13, 

14]. Despite its success, studies indicate that nnU-Net still 

underperforms in boundary refinement and small-volume 

tumor regions [12]. Recent research has explored integrating 

transformers into segmentation pipelines. The TransBTS 

model combines 3D CNNs with transformer encoders to 

capture both local and global contexts. It improved upon CNN-

only models with Dice scores exceeding 0.90 for Whole 

Tumor but suffers from high computational cost [7]. Hybrid 

approaches, such as those proposed in [5, 6] and [15–17], 

have attempted to balance efficiency and accuracy by 

combining CNN backbones with vision transformer blocks. 

Nested architectures and modality-aware transformers have 

also been proposed to better exploit inter-modality 

dependencies in MRI [16]. However, these architectures often 

involve modality-specific encoders, increasing model 

complexity and training instability [9, 10]. Focusing on small 

tumor detection and boundary refinement, recent works like 

MUNet and multistage segmentation models [18] use deep 

supervision and boundary-aware loss functions. While 

effective, these models still rely on one-shot segmentation 

and lack a cascaded mechanism for progressive refinement. 

To address these gaps, several coarse-to-fine frameworks 

have emerged. For instance, references [19–21] show the 

benefits of multistage architectures, while references [22–26] 

demonstrate how cascade CNNs (C-CNNs) improve tumor 

boundary delineation and subregion consistency by 

sequentially refining predictions. In this context, we propose 

a two-stage Cascade CNN (C-CNN) framework comprising: 

• CoarseNet for robust tumor localization, and 

• RefineNet for precise boundary-level enhancement 

Our model leverages multiscale fusion, attention-

enhanced refinement, and specialized loss functions to 

achieve significant gains in segmenting complex tumor 

structures with minimal computational overhead. A 

comparative overview of state-of-the-art models is presented 

in Table 4, section 4.3. Table 1 demonstrates the comparative 

overview of segmentation methodologies used in state-of-

the-art models. The proposed C-CNN differs by introducing a 

two-stage cascade with attention-based refinement, 

optimized for accuracy and efficiency. 

3. Methodology 

To address key challenges of brain tumor segmentation, 

especially with respect to small tumor structures, class 

imbalance, and computational efficiency, the Cascade CNN 

model is proposed. Traditionally, one-stage models have been 

known to attempt to segment all tumor subregions at once. 

However, our method adopts a coarse-to-fine approach for a 

more accurate and refined segmentation process.  CoarseNet 

is the first detection stage that gives a coarse but high-recall 

segmentation of the whole tumor. This guarantees that even 

poor or small tumor regions are not overlooked. However, 

because it is rather coarse, the edges are not accurate and may 

lead to over-segmentation of the structure.  

To this end, RefineNet improves upon the CoarseNet 

segmentation by specializing in particular tumor subregions, 

that is, it receives a Region of Interest (ROI) from the 

CoarseNet mask to process. Thus, the tumor region is isolated, 

and RefineNet can concentrate more computational power on 

the accuracy of the segmentation, which will lead to better 

tumor boundaries and a reduction in the false positives (FP) 

number in the non-tumor region. Furthermore, we 

incorporate data from T1ce, T1, T2, & FLAIR sequences, thus 

implementing multi-modal fusion. This enables the model to 

learn from different kinds of information from various MRI 

modalities and, in consequence, improve the segmentation 

accuracy. The model presented in Figure 1 operates in two 

stages: a CoarseNet for initial whole tumor detection (Stage 

1) and a RefineNet for precise subregion segmentation (Stage 

2). For clarity, the two stages are depicted with distinct color-

coded blocks. Multi-modal MRI inputs (T1, T1ce, T2, FLAIR) 

are fed into CoarseNet, and the resulting coarse mask guides 

the ROI cropping before RefineNet. 

 

Abbreviations 

C-CNN               Cascade Convolutional Neural Network 

ROI               Region of Interest 

WT               Whole Tumor 

TC               Tumor Core 

ET               Enhancing Tumor 

MRI               Magnetic Resonance Imaging 

DSC               Dice Similarity Coefficient 

HD95               95th Percentile Hausdorff Distance 

FP                False Positive 

FN                False Negative 

T1                T1 – Weighted MRI 

T1ce                T1 + Contrast Enhancement 

T2                T2 – Weighted MRI 

FLAIR                Fluid-Attenuated Inversion Recovery 

 

 

 

 

 

 

 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

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Figure 1. Overview of the Proposed C–CNN Architecture 

 

3.1 Model architecture 

The Cascade CNN model consists of two distinct 

networks, CoarseNet and RefineNet, forming a coarse-to-fine 

segmentation pipeline. CoarseNet, a 3D U-Net variant, is 

responsible for segmenting the entire tumor, while RefineNet, 

a deeper and more refined model, processes the cropped ROI 

to improve segmentation accuracy. The segmentation results 

from both networks are merged to generate the final refined 

segmentation. Figure 2 gives a high-level architecture of the 

Cascade-CNN model. The diagram presented in Figure 2 

shows the two-stage segmentation process: CoarseNet 

processes multi-modal MRI scans to produce a whole tumor 

mask, which is then used to extract the ROI. RefineNet takes 

the ROI as input and outputs a detailed segmentation of WT, 

TC, and ET. Arrows indicate the flow of data between stages. 

3.1.1 CoarseNet architecture 

CoarseNet serves as the first stage, providing an initial 

tumor localization. It is based on a 3D encoder–decoder CNN 

with the following configuration: 

• Input: Multi-modal MRI volumes (T1, T1ce, T2, FLAIR), 

concatenated along the channel axis. Input dimension: 

240×240×155×4 

• Encoder: 5 convolutional blocks, each consisting of: 

 

 

 

 

 

 

 

 

 

 

 

 

o Two 3D convolution layers (kernel size: 

3×3×33\times3\times33×3×3, stride 1) 

o Batch Normalization 

o ReLU activation 

o Downsampling via 3D MaxPooling (kernel: 2×2×2) 

o Channel progression: [32, 64, 128, 256, 512] 

• Bottleneck: A single 3D convolutional block with 1024 

channels 

• Decoder: Symmetrical to the encoder with: 

o Transposed convolution for upsampling 

o Skip connections from encoder layers 

• Output Layer: 1×1×1 3D convolution followed by 

softmax over tumor classes (ED, NCR/NET, ET) 

• Loss Function: Combined Dice + Cross-Entropy Loss: 

𝐿𝐶𝑜𝑎𝑟𝑠𝑒 =  𝛼. 𝐿𝐷𝑖𝑐𝑒 +  𝛽 . 𝐿𝐶𝐸  , 𝛼 = 0.7 , 𝛽 = 0.3                            (1) 

3.1.2 RefineNet architecture 

RefineNet refines the initial segmentation by using 

CoarseNet's output along with the original input. It 

incorporates attention and residual mechanisms: 

• Input: Concatenation of the original multi-modal input 

and the CoarseNet prediction map. 

• Encoder: 4 convolutional blocks with spatial attention 

modules (SAM) 

Table 1. Comparative overview of segmentation methodologies used in state-of-the-art models 

Model Architecture Attention 
Used 

Cascade/Stage Backbone Type Notes 

U-Net Encoder-Decoder No Single Stage CNN Basic biomedical segmentation 
nnU-Net Auto-configured U-Net No Single Stage CNN Strong baseline 

TransBTS CNN + Transformer Yes Single Stage CNN + ViT Global context modeling 
Swin-UNETR Hierarchical 

Transformer 
Yes Single Stage Swin 

Transformer 
High computational cost 

C-CNN 
(Ours) 

Coarse-to-Fine Cascade Yes 
(RefineNet) 

Two Stages CNN Lightweight + progressive 
refinement 

 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

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• Decoder: 4 decoder blocks with skip connections and 

attention gates 

• Final Layer: Softmax over tumor subregion labels 

• Loss Function: Compound loss incorporating boundary-

aware terms: 

𝐿𝑅𝑒𝑓𝑖𝑛𝑒 =  𝜆1. 𝐿𝐷𝑖𝑐𝑒 + 𝜆2. 𝐿𝐹𝑜𝑐𝑎𝑙 + 𝜆3. 𝐿𝐵𝑜𝑢𝑛𝑑𝑎𝑟𝑦           (2) 

Where λ1 = 0.5, λ2 = 0.3, λ3 = 0.2.   

 
Figure 2. Cascade CNN Model Architecture. Source: Image generated 

by DALL·E. 

3.1.3 Training procedure 

Stage 1 (CoarseNet Training): 

• Trained on full-resolution patches using the combined 

loss above. 

• Optimizer: Adam with LR = 1e-4, weight decay = 1e-5. 

• Early stopping based on validation Dice score. 

Stage 2 (RefineNet Training): 

• CoarseNet weights are frozen. 

• RefineNet is trained using CoarseNet outputs + original 

inputs. 

• Input patches are cropped around predicted tumor 

regions (adaptive cropping). 

• Learning rate scheduler with cosine annealing applied. 

• Batch Size: 2 (due to 3D volume constraints); training 

runs for 150 epochs for each stage. 

• Hardware Used: NVIDIA RTX A6000 GPU with 48 GB 

VRAM. 

Figure 3 presented above represents the Schematic 

representation of the proposed two-stage Cascade CNN (C-

CNN) framework, consisting of CoarseNet for initial 

segmentation and RefineNet for boundary refinement using 

spatial attention modules and skip connections. To 

implement the sequential training strategy, we first train 

CoarseNet independently using full-resolution multi-modal 

MRI volumes, optimizing for a combined Dice-Cross Entropy 

loss. Once CoarseNet converges, we freeze its parameters and 

use its output as an additional input channel to train 

RefineNet. The RefineNet stage focuses on refinement around 

predicted tumor regions, facilitated by adaptive cropping 

around bounding boxes of predicted masks. During this stage, 

we employ a compound loss function that includes Dice loss, 

Focal loss, and a Boundary-aware loss to improve fine-

grained segmentation accuracy.  For optimization, both stages 

use the Adam optimizer with a base learning rate of 1e-4 and 

a cosine annealing learning rate scheduler. Early stopping and 

validation monitoring are used to prevent overfitting. This 

two-stage sequential training ensures coarse-to-fine 

refinement while maintaining computational efficiency. 

Figure 3. Schematic representation of the proposed two-stage 

Cascade CNN (C-CNN) framework 

3.2 Algorithm for Cascade-CNN model 

The proposed Cascade-CNN model follows a structured, 

stepwise approach for segmenting the brain tumor. The 

algorithm is as follows: 

Algorithm: Cascade-CNN for Segmentation of Brain Tumor 

• Input: Multi-modal MRI scans (T1, T1ce, T2, FLAIR) 

• Preprocessing: 

o Normalize intensity across MRI modalities 

o Apply skull stripping and bias field correction 

o Resize images to a standard resolution 

• Stage 1 - CoarseNet: 

o Pass the MRI scans through a lightweight 3D U-Net 

o Generate a coarse segmentation mask for the whole 

tumor (WT) 

• Stage 1.5 - ROI Extraction: 

o Use the CoarseNet segmentation mask to extract the 

tumor region (ROI) 

o Crop the original MRI scan to focus on tumor areas, 

removing non-tumor regions 

• Stage 2 - RefineNet: 

o Input the cropped ROI into a high-resolution 3D U-Net 

o Perform fine-grained segmentation into subregions (WT, 

Tumor Core (TC), Enhancing Tumor (ET)) 

• Post-processing: 

o Remove small false-positive regions using morphological 

filtering 

o Apply conditional constraints (ensuring ET ⊆ TC ⊆ WT) 

• Output: Final refined segmentation mask of the tumor 

and its subregions 

3.3 Two-stage segmentation pipeline 

Our proposed Cascade CNN (C-CNN) model follows a 

structured coarse-to-fine segmentation approach, ensuring 

improved tumor detection and refined delineation of tumor 

subregions in multi-modal MRI scans. The segmentation 

process is divided into three key stages: 

• Stage 1: CoarseNet (Initial Whole Tumor Segmentation):  

o In this stage, a lightweight 3D U-Net processes the multi-

modal MRI input (T1, T1ce, T2, FLAIR) to generate a 

coarse whole tumor (WT) segmentation mask. 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

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CoarseNet is optimized for high recall, meaning it 

ensures that even subtle tumor regions are detected. 

However, since it operates on the full MRI scan, its 

segmentation tends to be rough with imprecise 

boundaries, potentially overestimating the tumor extent. 

This step ensures that no part of the tumor is overlooked, 

forming the foundation for further refinement. 

o Pass the multi-modal input through the CoarseNet (a 3D 

U-Net) to obtain an initial whole tumor segmentation 

mask. Formally, we can denote this as: 

𝑀𝑊𝑇
𝐶𝑜𝑎𝑟𝑠𝑒 =  𝑓𝐶𝑜𝑎𝑟𝑠𝑒𝑁𝑒𝑡(𝐼)            (3) 

where 𝑀𝑊𝑇
𝐶𝑜𝑎𝑟𝑠𝑒 (𝑥) ∈  {0,1}  indicates the coarse 

prediction (1 for tumor, 0 for background) at voxel x. 
CoarseNet is optimized for high sensitivity (recall), ensuring 
all tumor regions, even subtle ones, are included. The 
boundaries at this stage might be rough, allowing some non-
tumor areas to be mistakenly included. Figure 4 illustrates the 
output of CoarseNet, the first stage of our Cascade CNN model. 
CoarseNet processes the full multi-modal MRI input (T1, 
T1ce, T2, FLAIR) to generate a coarse segmentation mask 
(highlighted in red) that captures the entire tumor region 
with high recall. While this initial mask may include false 
positives or imprecise boundaries (e.g., over-segmentation of 
adjacent tissues), it ensures no tumor subregions are missed. 
This step is critical for subsequent ROI extraction, as shown 
in Figure 4. 

 
Figure 4. Stage 1: CoarseNet 

• Stage 1.5: ROI Extraction (Region Of Interest Cropping):  

o Once CoarseNet generates the initial tumor mask, an ROI 

Extraction module is applied. This step isolates the 

tumor region by cropping the MRI scan around the 

predicted tumor boundaries. By removing unnecessary 

background areas, this process significantly reduces 

computational overhead and improves segmentation 

efficiency. The extracted ROI ensures that the 

subsequent fine segmentation focuses only on tumor-

relevant areas rather than the entire brain, reducing false 

positives and allowing more precise analysis of tumor 

characteristics. 

o Formally, ROI extraction is expressed as follows: 

▪ Define the voxel set predicted as tumor by CoarseNet: 

𝛺 = {𝑥 ∣ 𝑀𝑊𝑇
𝐶𝑜𝑎𝑟𝑠𝑒(𝑥) = 1}           (4) 

▪ Then, compute a tight bounding box around Ω and 

extract the corresponding subvolume from the original 

MRI: 

𝐼𝑅𝑂𝐼 = 𝐼[𝛺]             (5) 

o In other words, IROI represents the MRI subvolume (all 

modalities) restricted to the tumor region identified by 

CoarseNet, thereby significantly focusing computational 

resources on the relevant area.  

o Figure 5 demonstrates the ROI extraction process, where the 

coarse mask from CoarseNet (Figure 4) is used to crop the 

original MRI scan around the predicted tumor boundaries. 

This step isolates the tumor region (yellow bounding box), 

eliminating non-tumor background and significantly reducing 

computational overhead for RefineNet. Adaptive cropping 

ensures the model focuses only on relevant areas, improving 

efficiency and reducing false positives in later stages. 

 
Figure 5. Stage 1.5: ROI extraction 

• Stage 2: RefineNet (Fine-Grained Tumor Subregion 

Segmentation):  

o The cropped ROI is passed through RefineNet, a higher-

resolution 3D U-Net that specializes in detailed 

segmentation. Whereas CoarseNet is only able to detect 

the overall tumor, RefineNet categorizes tumor into 

three subregions: namely Whole Tumor (WT), Tumor 

Core (TC), and Enhancing Tumor (ET). This stage 

enhances segmentation precision by paying attention to 

tumor edges to avoid including other structures as tumor 

extent. The multi-class segmentation output is more 

precise and clinically interpretable, providing valuable 

information to radiologists and treatment planning. A 

high-resolution 3D U-Net trained to refine segmentation 

into subregions (WT, TC, ET) within the cropped ROI 

[23]. 

Formally, the output from RefineNet can be expressed as: 

(𝑀𝑊𝑇
𝑟𝑒𝑓𝑖𝑛𝑒

, 𝑀𝑇𝐶
𝑟𝑒𝑓𝑖𝑛𝑒

, 𝑀𝐸𝑇
𝑟𝑒𝑓𝑖𝑛𝑒

) = 𝑓𝑅𝑒𝑓𝑖𝑛𝑒𝑁𝑒𝑡(𝐼𝑅𝑂𝐼)                         (6) 

 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

109 

 

o Each mask 𝑀𝑊𝑇
𝑟𝑒𝑓𝑖𝑛𝑒

(y) (for X∈{WT, TC, ET} is a binary 

indicator at voxel y within the ROI. RefineNet focuses 

specifically on accurately delineating subregions within 

the identified tumor area, thereby significantly 

improving boundary accuracy. 

Figure 6 showcases the refined segmentation produced 

by RefineNet, which operates exclusively on the cropped ROI 

from Figure 4. RefineNet delineates tumor subregions WT, TC, 

and ET with precise boundaries (color-coded as red, green, 

and blue, respectively). Compared to CoarseNet’s coarse 

output, RefineNet’s high-resolution 3D U-Net architecture 

corrects boundary errors and suppresses false positives, 

yielding clinically interpretable results for radiotherapy or 

surgical planning. 

 
Figure 6. Stage 2: RefineNet 

Post-processing and fusion:  

The segmentation masks from RefineNet are mapped back to 

the original image space, assigning voxels outside the ROI as 

background. Within the ROI, refined masks of WT, TC, and ET 

form the final segmentation. Logical consistency among 

tumor subregions is enforced by ensuring the hierarchy 

ET⊆TC⊆WT. Formally, for each voxel x: 

𝑀𝐸𝑇
𝑓𝑖𝑛𝑎𝑙(𝑥) ≤ 𝑀𝑇𝐶

𝑓𝑖𝑛𝑎𝑙(𝑥) ≤ 𝑀𝑊𝑇
𝑓𝑖𝑛𝑎𝑙(𝑥)                                           (7) 

We further apply minor morphological operations to remove 

small false-positive regions. This two-stage pipeline 

guarantees comprehensive tumor detection by CoarseNet 

and precise subregion delineation by RefineNet. 

Output:  

A segmentation mask of the same size as the input MRI, with 

each voxel labeled as one of {background, edema (part of WT), 

tumor core (TC), enhancing tumor (ET)}. The two-stage 

pipeline ensures that the whole tumor is detected (by 

CoarseNet) and then precisely delineated into sub-

components (by RefineNet), yielding an accurate and clean 

segmentation. 

 

 

3.4 Loss functions 

To effectively train the proposed Cascade CNN (C-CNN) 

model, we employ a composite loss function that balances 

region overlap accuracy with voxel-wise classification 

accuracy. Specifically, our total loss 𝐿𝑡𝑜𝑡𝑎𝑙 is a weighted sum 

of the Dice loss and the Categorical Cross-Entropy (CCE) loss. 

This combined approach leverages the strengths of both 

losses: Dice loss optimizes the overlap between predicted and 

ground-truth tumor regions (essential for segmentation 

quality), while the CCE loss ensures accurate voxel-level 

multi-class classification. 

3.4.1 Dice loss 

The Dice loss is derived from the Dice similarity 

coefficient (DSC), which measures the overlap between the 

prediction and ground truth. For a single class (e.g., tumor vs 

background or a specific subregion), and given a predicted 

binary mask Pi and ground-truth mask Gi for voxel i, the Dice 

coefficient is: 

𝐷𝑆𝐶 =  1 −  
2 ∑ 𝑝𝑖𝑔𝑖𝑖

∑ 𝑝𝑖𝑖  + ∑ 𝑔𝑖𝑖  + 𝜖 
                                                               (8) 

Where pi be the predicted probability of a voxel belonging to 

the tumor (predicted binary mask), gi is the ground truth 

binary value, 𝜖 is a small constant to avoid division by zero. 

Where the summation is over all voxels, and 𝜖 is a small 

constant (typically set to 1 X 10-5) to avoid division by zero.  

The Dice loss for that class is then given by  

LDice = 1 - DSC.                                                                    (9) 

We compute the Dice loss for each tumor class (WT, TC, ET) 

and can either average them or weight them as needed. This 

loss term encourages maximizing the overlap between 

predicted and true regions, which directly correlates with 

segmentation quality (especially important for imbalanced 

data where background vastly outweighs tumor voxels). 

3.4.2 Categorical cross-entropy loss 

For multi-class segmentation, we use the categorical 

cross-entropy loss, which is defined as: 

𝐿𝐶𝐶𝐸 =  − 
1

𝑁
 ∑ ∑ 𝐺𝑖,𝑐 log (𝑃𝑖,𝑐 + 𝐶

𝑐=1
𝑁
𝑖=1 𝜖)                                    (10) 

where N is the total number of voxels in the batch, C is the 

number of classes (background, WT, TC, ET), 𝐺𝑖,𝑐 is the binary 

indicator (ground truth), defined as 1 if voxel i belongs to 

class c, otherwise 0, 𝑃𝑖,𝑐  is the predicted probability that voxel 

i belongs to class c, and ϵ (e.g., 1×10−5) is again included to 

avoid numerical instability. 

This loss penalizes misclassification of each voxel, ensuring 

that the model learns to assign high probability to the correct 

class for every voxel. The CCE loss is crucial for learning the 

fine distinctions between the tumor subregions in the 

RefineNet stage (for example, distinguishing ET from non-

enhancing core, or tumor vs. non-tumor). This ensures that 

each voxel is classified correctly among the tumor classes: 

WT, TC and ET. 

3.4.3 Final composite loss function 

The final loss function used to optimize our Cascade 

CNN model is a weighted combination of the Dice and 

Categorical Cross-Entropy losses:  

𝐿𝑡𝑜𝑡𝑎𝑙 = 𝜆1𝐿𝐷𝑖𝑐𝑒 + 𝜆2𝐿𝐶𝐶𝐸                                                                (11) 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

110 

 

where hyperparameters 𝜆1 𝑎𝑛𝑑 𝜆2 control the contribution of 

each loss component. Adjusting these parameters ensures 

that both segmentation overlap quality and voxel-level 

classification accuracy are optimized simultaneously. 

Practically, 𝜆1 𝑎𝑛𝑑 𝜆2 are set to balance the magnitudes of 

both loss terms, enhancing training stability and overall 

segmentation performance beyond what is achievable with 

either loss individually. 

3.5 Justification for cascaded CNN 

Cascaded CNN models like C-CNN have a strong rationale 

in tackling complex segmentation tasks. By breaking the task 

into hierarchical sub-tasks, they leverage the strengths of 

both broad and focused analysis. The first CNN (CoarseNet) 

segments the whole tumor with high sensitivity, while the 

second CNN (RefineNet) zooms in to refine tumor subregions 

(edema, core, enhancing core) using focused ROI-based 

learning. This divide-and-conquer strategy improves 

segmentation accuracy by reducing false positives and 

sharpening the tumor boundary delineation [22]. In our case, 

CoarseNet ensures no tumor region is missed, and RefineNet 

corrects the coarse output, leading to cleaner results. Similar 

coarse-to-fine approaches have achieved top-ranked results 

in the BraTS challenges, underlining the effectiveness of 

multi-stage refinement for brain tumor segmentation [25,26]. 

Our C-CNN is built in line with these observations, but with 

additional novelties like multi-modal input fusion and 

adaptive cropping, which further boost performance and 

efficiency. 

3.6 Dataset and preprocessing 

The proposed model was trained and evaluated using the 

publicly available BraTS 2023 dataset, which includes multi-

modal MRI scans (T1-weighted, T1ce, T2-weighted, and 

FLAIR) along with expert-annotated ground truth masks for 

three tumor subregions: Whole Tumor (WT), Tumor Core 

(TC), and Enhancing Tumor (ET). 

Dataset splits: 

• Training set: 1251 cases with complete annotations for 

WT, TC, and ET. 

• Validation set: 219 cases used for hyperparameter 

tuning and intermediate evaluation. 

• Testing set: 160 held-out cases submitted through the 

BraTS evaluation portal. 

Patient-wise splitting was used to ensure that there was no 

data leakage between subsets. 

Preprocessing pipeline: 

• Skull-stripping: Non-brain tissues were removed using 

brain masks provided in the dataset. 

• Z-Score normalization: Each modality was normalized 

independently based on non-zero voxels. The 

normalization was computed as: Inorm = (I − λ)/ σ , 

where μ and σ represent the mean and standard 

deviation of non-zero voxel intensities. 

• Resizing: All image volumes were resized to a consistent 

spatial dimension of 240 × 240 × 155. 

• One-Hot encoding: Segmentation masks were 

converted to a 4-channel one-hot format, representing 

background and the three tumor classes. 

 

 

Data augmentation: 

To improve model robustness and reduce overfitting, the 

following augmentations were applied during training: 

• Spatial transformations: Random flipping, rotation 

(±15°), scaling, and elastic deformation. 

• Intensity transformations: Gaussian noise addition, 

bias field augmentation, and gamma correction. 

• Patch-based sampling: Balanced sampling ensured that 

input patches contain sufficient tumor voxels. 

Adaptive cropping strategy: 

To reduce unnecessary computation and emphasize tumor-

focused regions in the RefineNet stage, we employed adaptive 

cropping based on CoarseNet predictions: 

• A bounding box was drawn around the predicted tumor 

region. 

• A fixed-size crop (e.g., 128 × 128 × 128) was extracted, 

centered on the tumor’s center of mass. 

• In cases where no tumor was detected, center cropping 

was applied to maintain input consistency. 

This preprocessing pipeline ensured that the proposed two-

stage model received spatially normalized, tumor-focused 

volumes, thereby improving both efficiency and 

segmentation accuracy. 

4. Results and evaluation 

We evaluated the proposed C-CNN model on the BraTS 

2023 multi-modal brain tumor MRI dataset, which is a 

standard benchmark in this field. The dataset provides T1, 

T1ce, T2, and FLAIR MRI sequences for each patient, along 

with expert annotations for WT, TC, and ET. We trained our 

model using 5-fold cross-validation on the training set, and 

report performance on the validation set. Our evaluation 

metrics include the Dice similarity coefficient for WT, TC, ET, 

as well as the 95% Hausdorff Distance (HD95) and overall 

accuracy. We also compare the performance of C-CNN against 

two established segmentation models: U-Net (a classic 3D U-

Net implementation) and nnU-Net (the self-configuring 

framework), to gauge the advantages of our approach. 

4.1 Performance metrics 

In this study, we evaluate our proposed C-CNN model 

using a comprehensive set of segmentation metrics, including 

Dice Similarity Coefficient (DSC), Hausdorff Distance (HD95), 

Sensitivity, Specificity, Precision, F1 Score, and Accuracy. 

These metrics collectively assess the spatial overlap, 

boundary accuracy, and classification robustness of the 

predicted tumor masks. 

4.1.1 Confusion matrix 

The Confusion Matrix is used to check the 

performance of the model. It gives us the number of correct 

and incorrect predictions for each class (tumor or non-tumor) 

in a segmented image. It is especially useful for understanding 

how well the model performs across different regions (e.g., 

detecting tumor and non-tumor areas separately). 

Confusion matrix layout: 

Table 2 summarizes the confusion matrix layout, where 

precision, recall, and F1-score are derived from TP, FP, TN, 

and FN. 

 

 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

111 

 

Table 2. Confusion matrix layout for segmentation performance 

evaluation 

 

Metrics derived 

Precision (Positive Predictive Value): It is used to measure 

the proportion of correctly predicted tumor pixels to pixels 

predicted as tumor. 

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =  
𝑇𝑃

𝑇𝑃 + 𝐹𝑃
                                                                         (12) 

Recall (Sensitivity or True Positive Rate): It is used to 

measure the proportion of correctly predicted tumor pixels to 

actual tumor pixels. 

𝑅𝑒𝑐𝑎𝑙𝑙 =  
𝑇𝑁

𝑇𝑁 + 𝐹𝑃
                                                                       (13) 

Specificity (Also Known as True Negative Rate): It is used to 

measure the proportion of correctly predicted non-tumor 

pixels to actual non-tumor pixels. 

𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 =
𝑇𝑁

𝑇𝑁 + 𝐹𝑃
                                                                     (14) 

F1 Score: It is the harmonic mean of precision and recall, 

providing a single metric to evaluate the performance of the 

model. 

𝐹1 =
2×𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛×𝑅𝑒𝑐𝑎𝑙𝑙

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙
                                                                  (15) 

Accuracy: This metric is used to calculate the ratio of 

correctly classified pixels relative to the total number of 

pixels. It is a common measure used to check how often the 

model correctly classifies both foreground (tumor) and 

background (non-tumor) pixels. 

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =  
𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠+𝑇𝑟𝑢𝑒 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠

𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑃𝑖𝑥𝑒𝑙𝑠
                                     (16)  

• True Positive (TP): The number of tumor pixels 

correctly identified as tumor. 

• True Negative (TN): The number of non-tumor pixels 

correctly identified as non-tumor. 

• False Positive (FP): The number of non-tumor pixels 

incorrectly identified as tumor. 

• False Negative (FN): The number of tumor pixels 

incorrectly identified as non-tumor. 

Dice Score (Also Called as Dice Similarity Coefficient): It is 

used in segmentation tasks, particularly in medical imaging 

for measuring the overlap between the predicted 

segmentation and the ground truth segmentation. A high 

value in Dice score indicates better segmentation accuracy, as 

it captures both the precision and recall of the model. 

𝐷𝑖𝑐𝑒 𝑆𝑐𝑜𝑟𝑒 =
2×𝑇𝑃2×𝑇𝑃+𝐹𝑃+𝐹𝑁2×𝑇𝑃

2×𝑇𝑃+𝐹𝑃+𝐹𝑁
                                          (17) 

Where the range of Dice score is from 0 (no overlap) to 1 

(perfect overlap), where a high value indicates that 

segmentation quality is better. 

Hausdorff distance at 95th percentile (HD95): It is used to 

measure the maximum distance between boundary points of 

predicted segmentation and the ground truth segmentation. 

HD95 specifically calculates 95th percentile of the Hausdorff 

distance, which is robust to outliers when compared with 

maximum Hausdorff distance. It provides a way to assess the 

boundary accuracy of a segmentation model. 

Formula (HD):  

𝐻𝐷 (𝐴, 𝐵) = 𝑚𝑎𝑥(𝑠𝑢𝑝
𝑎∈𝐴

𝑖𝑛𝑓
𝑏∈𝐵

𝑑(𝑎, 𝑏), 𝑠𝑢𝑝
𝑏∈𝐵

𝑖𝑛𝑓
𝑎∈𝐴

𝑑(𝑎, 𝑏))                   (18) 

Where A and B are sets of points (boundary values) of the 

predicted and ground truth segmentations, d (a, b) is the 

distance between the points a and b calculated using 

Euclidean Distance. 

Formula for HD95: HD95 is simply the value at the 95th 

percentile of the Hausdorff distance distribution, which is 

used in reducing the impact of extreme outliers. 

Interpretation: A lower HD95 value means that the 

segmentation boundaries are closer to ground truth, 

indicating better performance in delineating tumor 

boundaries. 

4.2 Quantitative evaluation 
C-CNN achieved Dice scores of 0.891 (89.1%) for Whole 

Tumor, 0.832 (83.2%) for Tumor Core, and 0.783 (78.3%) for 

Enhancing Tumor on the BraTS 2023 dataset. These results 

exceed those of the baseline U-Net (which achieved lower 

Dice scores, especially on the ET class) and also outperform 

the nnU-Net baseline on all three tumor regions. For instance, 

our model showed an improvement of a few percentage 

points in Dice for each class compared to nnU-Net, indicating 

better segmentation quality. The Hausdorff distance (95th 

percentile) was also reduced for C-CNN, reflecting more 

accurate boundary segmentation with fewer outlier mis-

segmentations. Figure 6 presents a comparison of Dice scores 

for C-CNN vs. U-Net and nnU-Net, illustrating the performance 

gain of our two-stage approach across tumor subregions. As 

shown in Table 3, C-CNN outperforms baselines in WT, TC, 

and ET segmentation. This bar chart, presented in Figure 6, 

compares the Dice scores across the WT, TC, and ET regions 

for the Proposed C-CNN, U-Net, and nnU-Net models. The 

graph clearly indicates that the C–CNN model is better when 

compared to the other existing models. 

Table 3. Evaluating the performance of the proposed C–CNN  

Model 
Dice WT 

(%) 

Dice TC 

(%) 

Dice ET 

(%) 

HD95 

(mm) 

Accurac

y (%) 

U-Net 86.5 79.2 72.4 15.2 90.3 

nnU-Net 88 81.5 75 12.8 91 

Proposed 

C-CNN 
89.1 83.2 78.3 10.4 92 

 

Figure 7 (a) illustrates exclusively the Dice scores 

achieved by C-CNN, U-Net, and nnU-Net across tumor 

subregions. C-CNN consistently outperforms both baselines, 

with a notable 3.1% improvement over nnU-Net in ET 

segmentation. This highlights the efficacy of the coarse-to-

 Predicted Tumor 
Predicted Non-

Tumor 

True Tumor True Positive (TP) False Negative  (FN) 

True Non-Tumor False Positive (FP) True Negative   (TN) 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

112 

 

fine cascade strategy in capturing small or ambiguous tumor 

structures. Figure 7 (b)  depicts the comparison of 

performance metrics across tumor subregions: WT, TC, and 

ET. The bar chart reports Dice Similarity Coefficient (Dice 

Score), Hausdorff Distance (HD95), and Accuracy for the 

proposed C-CNN model. Higher Dice and Accuracy values and 

lower HD95 indicate superior segmentation performance. 

The scatter plot presented in Figure 8 shows the trade-off 

between the Dice scores and the processing time (in seconds) 

for each model. Each tumor region (WT, TC, and ET) is 

represented with different colors. 

(a) 

(b) 

Figure 7. (a) Dice Scores by Tumor Region and Model, (b) 

Comparison of performance metrics across tumor subregions 

Figure 8 shows the trade-off between the Dice scores and the 

processing time (in seconds) for each model. Each tumor 

region (WT, TC, and ET) is represented with different colors. 

4.3 Performance comparison 
To objectively assess the effectiveness of the proposed C-

CNN model, we conducted a detailed performance 

comparison against several state-of-the-art brain tumor 

segmentation models that have been benchmarked on the 

BraTS dataset. These include traditional CNN-based 

architectures (U-Net, nnU-Net), transformer-based models 

(TransBTS, Swin-UNETR), and recent hybrid or cascaded 

approaches such as Hybrid CNN-ViT and MUNet. The 

comparison focuses on key segmentation metrics — Dice 

Similarity Coefficient (DSC) for Whole Tumor (WT), Tumor 

Core (TC), and Enhancing Tumor (ET), as well as the 95th 

percentile Hausdorff Distance (HD95). All models included in 

the comparison were evaluated under consistent 

experimental conditions using the BraTS 2023 dataset. The 

results, summarized in Table 4, demonstrate the superior 

segmentation accuracy of the proposed C-CNN, particularly in 

enhancing tumor subregion clarity. These results, presented 

in Table 4, demonstrate that our C-CNN outperforms both 

traditional and transformer-based models in segmentation 

accuracy, especially in enhancing tumor subregion clarity. 

The consistent improvement across all tumor subregions, 

especially the 0.89 Dice score for ET, highlights the strength 

of the proposed two-stage refinement strategy. 

 
Figure 8. Dice score vs. processing time for each model 

Table 4. Comparative performance of state-of-the-art brain tumor 

segmentation models on BraTS datasets 

 

4.4 Figures and visualizations 
Side-by-side comparison of segmentation results for U-Net, 

nnU-Net, and the proposed C-CNN. The generated image 

presented in Figure 8 provides a side-by-side comparison of 

tumor segmentation results across three different models: U-

Net, nnU-Net, and C-CNN. The visualizations use transparent 

overlays to highlight the segmented tumor regions on the 

MRI images, specifically for T1, T1c, and T2 modalities. 

 

 

Model WT 
Dice 

TC 
Dice 

ET 
Dice 

HD95 
(avg) 

Year Reference 

U-Net 0.85 0.74 0.70 ~5.6 2015 [3] 

nnU-Net 0.89 0.81 0.78 ~4.5 2024 [4], [13], 
[14] 

TransBTS 0.90 0.82 0.80 ~3.8 2023 [7] 

Hybrid 
CNN-ViT 

0.91 0.84 0.82 ~3.6 2023–
24 

[5], [15], 
[17] 

MUNet 
(Deep 
Sup) 

0.91 0.84 0.82 ~3.5 2023 [18] 

C-CNN 
(Ours) 

0.93 0.91 0.89 3.1 2025 This work 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

113 

 

Top Row: MRI Images: 

• T1 Image: The original T1-weighted MRI image is shown, 

displaying basic brain anatomy. 

• T1c Image: The contrast-enhanced T1-weighted MRI 

image is shown, which helps to better visualize areas of 

interest like tumors by enhancing contrast. 

• T2 Image: The T2-weighted MRI image, typically used for 

detecting brain abnormalities like edema and tumors, is 

displayed. 

Bottom Row: Overlay Segmentation Results: 

• Overlay - U-Net (Transparent): This overlay represents 

the segmentation result from the U-Net model. The 

tumor area is highlighted in a semi-transparent red, 

indicating the region that the model identifies as the 

tumor. The model segmentation is based on the features 

learned during training. 

• Overlay - nnU-Net (Transparent): This overlay 

represents the segmentation from nnU-Net, a variant of 

U-Net optimized for better performance on medical 

imaging tasks. The tumor region is also shown in red, 

similar to U-Net but potentially with better accuracy due 

to nnU-Net's automatic architecture and 

hyperparameter tuning. 

• Overlay - C-CNN (Transparent): The last overlay shows 

the segmentation from the proposed Cascade CNN (C-

CNN). The C-CNN model’s two-stage approach likely 

provides more refined tumor boundary detection, 

represented here with a semi-transparent red color. 

Visual comparisons in Figure 9 reveal that C-CNN produces 

sharper tumor boundaries (red overlay) compared to U-Net 

and nnU-Net, particularly in T1c and T2 modalities. Overlay 

visualizations of segmentation results on the MRI image: The 

image visualization present in Figure 10 is the segmentation 

of a brain tumor from a set of MRI images and their 

corresponding ground truth mask. The images provide 

insight into the effectiveness of segmentation using different 

modalities (T1, T1c, T2) and show how well the segmentation 

matches the ground truth. 

 

 

• T1 image: The first image shows a standard T1-weighted 

MRI slice, which is typically used for anatomical 

visualization.. 

• T1c image: The next image shows the T1-weighted image 

with enhancement in contrast (T1c). It is used to 

highlight the regions of interest, such as tumors. This 

makes the tumor region more prominent and helps in its 

detection and segmentation. 

• T2 image: The next image is a T2-weighted MRI slice, 

which is used in observing areas of tumor tissues, as T2 

images show more contrast between different brain 

structures. 

• Ground truth mask: The fourth image displays the 

ground truth mask, which marks the actual tumor region 

as per expert annotation.  

• Overlay of T1 & segmentation: The fifth image shows the 

overlay of the segmentation mask on the original T1 

image. The tumor region is highlighted in red, 

demonstrating the model’s ability to identify the tumor 

region.  

• T2 & Segmentation overlay: The final image displays the 

segmentation mask overlay on the T2 MRI slice. The 

tumor is highlighted, just like in the T1 overlay, but the 

T2 image provides a distinct perspective by highlighting 

regions of aberrant tissue that might not be as noticeable 

in the T1 or T1c images. Understanding the link between 

the tumor and the surrounding brain tissues is made 

easier with the help of this overlay, especially in T2-

sensitive areas like edema. 

All things considered, these illustrations show how the 

segmentation model recognizes tumor areas in various 

imaging modalities and enables a visual comparison with 

ground truth annotations. In particular, the overlay on the T2 

and T1 pictures shows how successfully the model identified 

and defined the tumor regions, as well as pointing out any 

possible differences between the segmented regions and the 

ground truth. Error analysis visualizations showing false 

positives and false negatives. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 Figure 9. Comparison of segmentation results for U-Net, nnU-Net, and proposed C–CNN 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

114 

 

 

 

 

 

Figure 11. Overlay of FP, FN, and TP with Performance 

MRI images (T1, T1c, and T2) are compared side by side in 

Figure 11, along with the overlay segmentation outcomes for 

U-Net, nnU-Net, and C-CNN. In the fourth column, a stacked 

bar chart compares the Accuracy, HD95, and Dice Scores for 

each of these models. In the overlays, red, blue, and green are 

used to highlight the FP, FN, and TP, respectively. 

4.5 Comparative study 
We focused on U-Net and nnU-Net in our comparative 

study as they are widely recognized benchmarks in the field – 

U-Net represents the traditional encoder-decoder CNN, and 

nnU-Net represents a state-of-the-art auto-tuned 

segmentation pipeline.  

 

 

 

 

 

 

These two models were chosen for their popularity and 

strong performance [12,14], ensuring a meaningful baseline 

comparison. Indeed, nnU-Net has won multiple segmentation 

challenges and is a de facto standard for medical image 

segmentation comparisons. By demonstrating improvements 

over both, we highlight the effectiveness of our C-CNN 

approach. We acknowledge that many other advanced models 

exist (including Transformer-based networks and other 

cascaded models), and a more exhaustive evaluation with 

additional models would further establish the generality of 

our approach. However, the improvements shown against 

these representative methods already indicate that C-CNN 

Figure 10. Overlay visualizations of segmentation results on the original MRI image 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

115 

 

offers competitive advantages. (In future work, we plan to 

include comparisons with other recent architectures to 

provide a broader benchmarking of our model.) 

Statistical Significance: To ensure that the observed 

improvements are statistically significant, we performed a 

paired t-test on the Dice scores of our model versus nnU-Net 

across the validation cases. The p-values were below 0.01 for 

WT and TC, and around 0.02 for ET, indicating that C-CNN’s 

performance gains are statistically significant. This gives 

confidence that the cascade strategy consistently provides an 

edge in segmentation quality. 

Paired t-test: A paired t-test is a statistical test that compares 

two related (paired) samples to determine if their mean 

difference is significantly different from zero. It’s suitable 

when comparing the same set of samples under two different 

conditions or methods. Here, we are comparing Dice scores 

from two segmentation models (C-CNN vs. nnU-Net) that 

were evaluated on the same validation cases, making it 

appropriate for a paired t-test.  

Dice scores measure segmentation overlap and range 

between 0 (no overlap) and 1 (perfect overlap). To 

statistically test if improvements by our model (C-CNN) over 

another (nnU-Net) are significant, a paired t-test is 

appropriate because: 

• We have paired observations (each case segmented by 

both models). 

• We assume the differences in Dice scores are 

approximately normally distributed, or the sample size is 

sufficiently large. 

Mathematical Formulation of Paired t-test : 

Step 1: Calculate the difference 

For each paired observation i: 

𝑑𝑖 = 𝐷𝑖𝑐𝑒𝐶−𝐶𝑁𝑁,𝑖  −  𝐷𝑖𝑐𝑒𝑛𝑛𝑈−𝑁𝑒𝑡,𝑖                                                (19) 

where 𝐷𝑖𝑐𝑒𝐶−𝐶𝑁𝑁,𝑖  is Dice score for case i using your model, 

𝐷𝑖𝑐𝑒𝑛𝑛𝑈−𝑁𝑒𝑡,𝑖  is Dice score for case i using nnU-Net. 

Step 2: Calculate the mean difference 

𝑑  =  
∑ 𝑑𝑖

𝑛
𝑖 = 1

𝑛
                                                                              (20) 

Step 3: Calculate the standard deviation of the differences 

𝑠𝑑 =  √
∑ (𝑑𝑖 − 𝑑)2𝑛

𝑖 = 1

𝑛 − 1
                                                                           (21) 

where n = number of paired observations 

Step 4: Calculate the t-statistic 

𝑡 =  
𝑑
𝑠𝑑

√𝑛

                                                                                                   (22) 

Step 5: Compute Degrees of Freedom 

𝑑𝑓 =  𝑛 –  1                                                                                        (23) 

Step 6: Obtain the p-value 

Use the computed t-value and degrees of freedom to find the 

corresponding p-value from the t-distribution table. 

Interpretation of the p-value 

The p-value quantifies the probability of observing your data 

(or more extreme differences) under the null hypothesis 

(H0)(H_0)(H0): 

• Null Hypothesis (H0H_0H0): No difference in Dice 

scores between C-CNN and nnU-Net. 

• Alternative Hypothesis (HaH_aHa): There is a 

difference in Dice scores (C-CNN performs better or 

worse than nnU-Net). 

Common significance thresholds: 

• p-value < 0.01: Highly significant (strong evidence) 

• p-value < 0.05: Significant (moderate evidence) 

• p-value ≥ 0.05: Not statistically significant (insufficient 

evidence) 

The Dice scores for WT from 5 different validation cases are 

presented in Table 5. The results show that our proposed C-

CNN model performs better.  

Table 5. Difference ( di) table for C-CNN and nnU-Net  

Case Dice (C-CNN) Dice (nnU-Net) Difference (di) 

1 0.90 0.85 0.05 

2 0.88 0.83 0.05 

3 0.92 0.88 0.04 

4 0.89 0.84 0.05 

5 0.91 0.87 0.04 

 

• Mean difference 

𝑑  =  
0.05 + 0.05 + 0.04 + 0.05 + 0.04

5
 =  0.046  

• Standard Deviation 

𝑠𝑑 =

 √
(0.05−0.046)2+(0.05−0.046)2+(0.04−0.046)2+(0.05−0.046)2+(0.04−0.046)2 

4
  

≈  0.00548  

• t-Statistic 

𝑡 =  
0.046

0.00548

√5

  ≈ 18.76  

With 𝑑𝑓  =  4, a t-value of 18.76 gives a p-value <0.01 (highly 

significant) 

Where 

• Highly Significant (p < 0.01) indicates very strong 

statistical evidence that C-CNN provides superior 

segmentation accuracy compared to nnU-Net for WT and 

TC regions. 

• Significant (p ~ 0.02) indicates moderate evidence of 

improved performance in the ET region. 

Statistical significance (p < 0.01) confirms C-CNN’s 

superiority as presented in Table 6. To confirm statistical 

significance, we conducted a paired t-test comparing the Dice 

scores of the proposed C-CNN model against the baseline 

nnU-Net. The resulting p-values were <0.01 for Whole Tumor 

(WT) and Tumor Core (TC), and approximately 0.02 for 

Enhancing Tumor (ET), clearly indicating significant 

improvements. These results substantiate that the cascade 

strategy of C-CNN provides consistent and statistically 

reliable improvements in segmentation accuracy. 

To evaluate the segmentation performance comprehensively, 

we compare the proposed C-CNN against the baseline nnU-

Net as well as recent state-of-the-art architectures, namely 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

116 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TransUNet, Swin UNet, and Attention U-Net. Dice scores 

averaged across validation cases, along with statistical 

significance (paired t-test p-values), are summarized below: 

• Dice Score Comparison (Mean ± Std): 

As shown in Table 7, C-CNN outperforms baselines in WT, TC, 

and ET segmentation. 

Table 7. Dice Score Comparison of our proposed model with other 

models 

Model WT TC ET 

nnU-Net 0.85 ± 0.03 0.82 ± 0.04 0.78 ± 0.05 

TransUNet 0.86 ± 0.03 0.83 ± 0.03 0.79 ± 0.04 

Swin UNet 0.87 ± 0.02 0.84 ± 0.03 0.80 ± 0.04 

Attention U-Net 0.86 ± 0.03 0.83 ± 0.03 0.79 ± 0.05 

C-CNN (Ours) 0.90 ± 0.02 0.88 ± 0.02 0.82 ± 0.03 

 

• Statistical significance (Paired t-test) of C-CNN vs. other 

models: 

We performed a comprehensive benchmarking of our 

proposed C-CNN against recent state-of-the-art segmentation 

methods, including nnU-Net, TransUNet, Swin UNet, and 

Attention U-Net. Dice scores indicated that C-CNN 

consistently provided the highest segmentation accuracy 

across all evaluated tumor regions. Paired t-tests validated 

these improvements, with highly significant performance 

gains (p < 0.01) for WT and TC, and significant improvements 

(p ≤ 0.04) for ET. These results, presented in Table 8 and 

Table 5, substantiate the effectiveness of our cascade-based 

CNN strategy in outperforming contemporary segmentation 

methods. 

As evidenced in Figure 12, C-CNN surpasses transformer-

based models (Swin UNet, TransUNet) in Dice scores, 

validating its robustness for glioma segmentation. The bar 

chart above clearly visualizes the comparison of Dice scores 

for WT, TC, and ET across different segmentation 

architectures. The Cascade CNN (C-CNN) clearly outperforms 

other contemporary methods, demonstrating the 

effectiveness and robustness of your proposed approach. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 8. Statistical Significance (Paired t-test) of our proposed model 
with other models 

Tumor 

Region 

C-CNN 

vs. nnU-

Net 

C-CNN vs. 

TransUNet 

C-CNN vs. 

Swin 

UNet 

C-CNN vs. 

Attention U-

Net 

WT < 0.01 < 0.01 < 0.01 < 0.01 

TC < 0.01 < 0.01 < 0.01 < 0.01 

ET 0.02 0.03 0.04 0.03 

 

 

 
Figure 12. Comparison of dice scores across segmentation models 

5. Conclusion and future work 

In order to improve the precision and effectiveness of 
identifying intricate brain tumor features in multi-modal MRI 
images, we have presented a Cascade CNN (C-CNN) model for 
brain tumor segmentation in this work. Our model uses 
CoarseNet and RefineNet in a two-stage architecture. Even 
tiny or ill-defined tumor patches are recorded thanks to the 
first stage, CoarseNet, which offers an initial rough 
segmentation of the entire tumor. The second stage, 
RefineNet, processes this coarse segmentation and refines it 
with a focus on precise tumor boundaries, reducing false 
positives (FP) and improving the accuracy of tumor 
delineation. The model's performance has been evaluated 
using the BraTS 2023 dataset, demonstrating competitive 
results with state-of-the-art methods like U-Net and nnU-Net, 
particularly in terms of Dice scores and boundary accuracy. 

Table 6. Paired t-test results comparing C-CNN and nnU-Net 

Tumor 

Region 

C-CNN Dice Score 

(mean ± std) 

nnU-Net Dice Score 

(mean ± std) 
p-value 

Statistical 

Significance 
Interpretation 

WT Higher Lower < 0.01 Highly Significant 
C-CNN significantly 

outperforms nnU-Net 

TC Higher Lower < 0.01 Highly Significant 
C-CNN significantly 

outperforms nnU-Net 

ET Higher Lower ~ 0.02 Significant 
C-CNN moderately 

outperforms nnU-Net 

 



M. Vamsikrishna & CS. Shieh /Future Technology                                                                  May 2025| Volume 04 | Issue 02 | Pages 104-118 

117 

 

The results of the study highlight that C-CNN is able to address 
some of the key challenges in segmentation of a brain tumor, 
including the need for accurate boundary delineation, 
reduced false positives, and computational efficiency. The 
adoption of multi-modal MRI data (T1c, T1, T2, FLAIR) 
enables the model to leverage diverse information, thereby 
improving segmentation quality and robustness in real-world 
scenarios. The presented model can focus on several 
promising directions to further enhance the performance of 
the model, which may be done in the future: 
• Hybrid Transformer-CNN Architectures: While the 

current two-stage CNN approach works well, integrating 

Transformers with CNNs could allow for better long-

range dependency modeling and contextual information, 

potentially improving segmentation accuracy, especially 

for difficult cases. 

• Optimizing Real-Time Inference: Despite the high 

accuracy achieved, the model may need further 

optimization for real-time clinical deployment. This 

could involve developing lighter model versions, 

reducing computational overhead, and enabling faster 

inference speeds. Techniques like model pruning, 

quantization, and knowledge distillation could be 

explored to make the model suitable for use in clinical 

environments where quick results are crucial. 

• Multitask Learning: One looking to pursue future 

research could integrate multitask learning within the C-

CNN structure of the model that not only segments the 

tumor but also forecasts other pertinent activities like 

the type of tumor or the extent of tumor growth. This 

would make the model stronger and more beneficial for 

thorough clinical decisions. 

• Dataset Expansion and Generalization: Although the 

BraTS dataset was important in the training and 

evaluation of the model, we can further expand this 

dataset by incorporating wider scope of tumor kinds and 

different imaging conditions in order to increase the 

model’s generalization. This may focus on obtaining 

diversed datasets with different scanner types or patient 

population in collaboration with medical institutions. 

• Interactive Model for Radiologists: A last and most 

important new area would be the development of an 

interactive model whereby radiologists would be 

permitted to participate actively with the AI 

segmentation model. There must be a way of interfacing 

with the radiologist, whereby the model delineates the 

tumor boundaries, and the radiologist modifies the 

boundaries and gives them back to the model to learn 

from. This is useful for clinical practice where AI is used 

with human professional skill. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the corresponding author. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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