A systematic review of machine learning and deep learning approaches for gastrointestinal cancer diagnosis
Abstract
Globally, one of the prominent causes of cancer-related deaths is gastrointestinal cancer. It includes the tumour in the regions of the gastrointestinal tract, such as the esophagus, stomach, liver, pancreas, and colon. Improving patient outcomes requires an early and accurate diagnosis, but traditional diagnostic techniques are frequently laborious and subjective. Across various modalities, machine learning and deep learning techniques have become effective solutions for computerized diagnostics, categorization, and lesion segmentation. Through an emphasis on the larger category of gastrointestinal cancer rather than specific cancer types, this survey offers a thorough review of machine learning and deep learning implementations in gastrointestinal cancer diagnosis. When gastrointestinal cancers are reviewed collectively, common imaging and pathological patterns can be found, model cross-learning is facilitated, and the creation of broadly applicable diagnostic frameworks is supported. Publicly accessible datasets, important assessment metrics, and challenges, such as restricted dataset diversity, no external validation, and limited model interpretability, are highlighted in this paper. The review finds common strengths and limitations of current machine learning and deep learning approaches by synthesizing studies across various gastrointestinal cancers. It also describes future research directions that can enhance the real-world clinical implementation and reliability, including multimodal data usage, self-supervised and federated learning, and interpretable AI frameworks.