A Comparative Study of Classical and Deep Learning Approaches for Colorectal Cancer using Histopathology Images

Abstract

This survey paper covers the in-depth analysis of the application of the classical machine learning models and modern deep learning models for colorectal cancer (CRC) analysis using histopathology images. In last decade, researchers have increasingly used computational methods in pathology and applying variety of algorithms to improve diagnostic accuracy and prognostic evaluation. Classical methods involved the use of feature extraction and traditional classifiers like support vector machines (SVM) and random forests (RF). While, deep learning approaches, and in particular convolutional neural networks (CNNs), have achieved better performance by learning hierarchical representations directly from image data. By synthesizing evidence from 25 authentic studies, this paper critically compares these two paradigms, elucidating their methodological differences, performance metrics, and clinical applicability in the field of histopathology analysis of colorectal cancer. We describe preprocessing approaches such as stain normalization and augmentation as well as challenges such as small annotated datasets and variability in tissue preparation. It further talks about the Hybrid models, which may include both classical and deep learning features to improve beyond accuracy. Finally, we discuss emerging trends, future directions, and limitations. As noted, in the analysis, deep learning demonstrates a considerable potential but classical approaches still provide a competitive edge in environments with limited availability of data or when interpretability of model decisions are required. In conclusion, this work presents a comprehensive survey to help the research community as well as clinicians choose the right approaches for colorectal cancer detection and prognosis analysis from histopathology images.