id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
easat-10839	Wasito, Ito; Santoso, Handri; F, Denny Saptono; Faozi, Ekan	Fully automated tumor-stroma ratio prediction as prognosis factor in colorectal cancer using optimized deep transfer learning	2025	13	.pdf	application/pdf	4698	250	48	Conclusions The applications of various architectures of Convolutional Neural Networks (CNN), such as Deep Residual Networks (ResNet), Inception Networks, Densely Connected Convolutional Networks (DenseNet), MobileNet, and EfficientNet, for colorectal cancer image classification and automated Tumor-Stroma Ratio (TSR) prediction have been successfully implemented. Published: 3 November 2025 * Correspondence: ito.wasito@pradita.ac.id Fully automated tumor-stroma ratio prediction as prognosis factor in colorectal cancer using optimized deep transfer learning Ito Wasito1*, Handri Santoso2, Denny Saptono F2, Ekan Faozi3 1Department of Information Technology, Pradita University, Banten, Indonesia; ito.wasito@pradita.ac.id (I.W.).	cache/easat-10839.pdf	txt/easat-10839.txt