Classification of colorectal cancer in histological images using deep neural networks: an investigation

Abstract

Colorectal cancer refers to cancer of the colon or rectum; and has high incidence rates worldwide. Colorectal cancer most often occurs in the form of adenocarcinoma, which is known to arise from adenoma, a precancerous lesion. In general, colorectal tissue collected through a colonoscopy is prepared on glass slides and diagnosed by a pathologist through a microscopic examination. In the pathological diagnosis, an adenoma is relatively easy to diagnose because the proliferation of epithelial cells is simple and exhibits distinct changes compared to normal tissue. Conversely, in the case of adenocarcinoma, the degree of fusion and proliferation of epithelial cells is complex and shows continuity. Thus, it takes a considerable amount of time to diagnose adenocarcinoma and classify the degree of differentiation, and discordant diagnoses may arise between the examining pathologists. To address these difficulties, this study performed pathological examinations of colorectal tissues based on deep learning. The approach was tested experimentally with images obtained via colonoscopic biopsy from Gyeongsang National University Changwon Hospital from March 1, 2016, to April 30, 2019. Accordingly, this study demonstrates that deep learning can perform a detailed classification of colorectal tissues, including colorectal cancer. To the best of our knowledge, there is no previous study which has conducted a similarly detailed feasibility analysis of a deep learning-based colorectal cancer classification solution.