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research-article

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

Published: 01 November 2021 Publication History

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.

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Cited By

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  • (2023)Deep Learning-based Histopathological Image Classification of Colorectal Cancer: A Brief Survey of Recent TrendsProceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems10.1145/3638209.3638230(139-146)Online publication date: 25-Nov-2023
  • (2023)Improving deep learning-based polyp detection using feature extraction and data augmentationMultimedia Tools and Applications10.1007/s11042-022-13995-682:11(16817-16837)Online publication date: 1-May-2023

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          Published In

          cover image Multimedia Tools and Applications
          Multimedia Tools and Applications  Volume 80, Issue 28-29
          Nov 2021
          1140 pages

          Publisher

          Kluwer Academic Publishers

          United States

          Publication History

          Published: 01 November 2021
          Accepted: 13 January 2021
          Revision received: 22 December 2020
          Received: 19 June 2020

          Author Tags

          1. Deep learning
          2. Colorectal cancer
          3. Adenocarcinoma

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          • the GRRC program of Gyeonggi province.

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          View all
          • (2023)Deep Learning-based Histopathological Image Classification of Colorectal Cancer: A Brief Survey of Recent TrendsProceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems10.1145/3638209.3638230(139-146)Online publication date: 25-Nov-2023
          • (2023)Improving deep learning-based polyp detection using feature extraction and data augmentationMultimedia Tools and Applications10.1007/s11042-022-13995-682:11(16817-16837)Online publication date: 1-May-2023

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