Detection and Classification of Colorectal Cancer Types Using Deep Residual Learning based on ResNet-50 with Adam Optimization Method

Authors
Herman Omer 1
Salwa M. Hasan 2
Lozan M. Abdullrahman
Amira Bibo
1 IT Department, Duhok College Technique, Duhok Polytechnic University, Duhok 42001, Kurdistan Region, Iraq.
2 Food Production and Technology Department, Duhok College Technique, Duhok Polytechnic University, Duhok 42001, Kurdistan Region, Iraq.
10.24271/psr.2026.244509
Abstract
These days, digital pathology plays a crucial role in tumor detection and prognosis, particularly in objectively evaluating histological images for colorectal cancer (CRC). Deep learning models have demonstrated significant success in image classification, prompting researchers to adopt these methods for medical image analysis. This research explores deep learning methods, specifically using ResNet architectures (ResNet-18, ResNet-50, ResNet-101), combined with various optimization methods, including Adam, Stochastic Gradient Descent with Momentum (SGDM), and Root Mean Square Propagation (RMSProp), for classifying colorectal cancer types from histological images. A comprehensive evaluation was conducted to identify the most effective neural network architecture and optimization approach. Among the tested combinations, the ResNet-50 model with the Adam optimization method achieved the highest accuracy of 99.86% on the NCT-CRC-HE-7K dataset and an accuracy of 98.36% on the
NCT-CRC-HE-100K dataset, outperforming other tested combinations and existing approaches in the literature, effectively differentiating between benign and aggressive colorectal cancer. Lastly, the suggested model was compared to the current models, which were assessed to ascertain the most effective neural network models and the best training approach for our case study on colon tumor segmentation.
Keywords
Colorectal Cancer
Deep Residual Learning
ResNet-50
Adam Optimization
Crossmark
Subjects
Passer Journal of Basic and Applied Sciences
Volume 8, Special Issue
Proceedings of the 5th International Conference on Advanced science & Engineering ICOASE 2026, University of Zakho and Duhok Polytechnique University, 00th – 00th September 2026
August 2026
Pages 198-213