A systematic review on colon cancer classification by convolutional neural networks: Architecture, accuracy, and research directions

Abstract

Introduction Recent studies indicated the advancement of Convolutional Neural Networks (CNNs) have facilitated the colon cancer diagnosis process via medical image processing and multi-modality prediction analysis. This paper aims to systematically review the recent notable studies of CNN-based colon cancer classification using histopathological WSI and compare their strengths and limitations as inspirations for future research directions. Method This systematic review was conducted under a PRISMA analysis framework. The PICO selection and PROBAST bias assessment tools were used in data selection and validation. The systematic review compared accuracy from notable studies in benchmark architectures (Res-Net, Inception, VGG-Net, Dense-Net, and Efficient-Net) regarding the bi- and multi-classification tasks (2020-2025). Results The result showed that Res-Net architecture demonstrated the highest accuracy in both bi- (99.97%) and multi-classification (99.96%) tasks over the past five years. Newer architectures (Efficient-Net, Dense-Net) outperformed older models (VGG-Net, Res-Net) by optimizing depth and feature reuse while minimizing biases. Low computational model were more suitable for real-world deployment and clinical interaction. Conclusion This review contributes to the systematic synthesis of knowledge regarding advancements in CNN-based colon cancer classification. Also, this paper provided future research guidelines for further directions (quantum AI, advanced analytics, and lightweight integrations) and implementation in clinical settings.