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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261451997","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T05:12:02Z","timestamp":1783660322000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A systematic review on colon cancer classification by convolutional neural networks: Architecture, accuracy, and research directions"],"prefix":"10.1177","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4046-5746","authenticated-orcid":false,"given":"Jie","family":"Li","sequence":"first","affiliation":[{"name":"Taylor\u2019s University"},{"name":"Hebei Academy of Fine Arts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Goh","sequence":"additional","affiliation":[{"name":"Taylor\u2019s University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8116-4733","authenticated-orcid":false,"given":"N. 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