Deep Learning for Colon Cancer Classification: A Comparative Review of State-of-the-Art Architectures and Emerging Trends

Abstract

Colon cancer is one of the leading causes of cancer-related mortality worldwide, necessitating early and accurate detection for improved patient outcomes. Deep learning has revolutionized medical image analysis, particularly in histopathology-based classification of colon cancer. This review provides a comprehensive analysis of state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid models. We compare their performance, strengths, and limitations based on recent advancements in medical AI research. The study highlights the role of attention mechanisms, self-supervised learning, federated learning, and explainability techniques such as Grad-CAM in enhancing model reliability and interpretability. Furthermore, emerging trends such as contrastive learning, diffusion models, and Capsule Networks are explored for their potential in improving classification accuracy. Challenges such as data scarcity, generalization issues, and computational demands are also discussed. This review aims to provide insights into the evolution of deep learning for colon cancer classification and outlines future research directions to bridge existing gaps.