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

  • 1171: Real-time 2D/ 3D Image Processing with Deep Learning
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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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References

  1. Awan R, Sirinukunwattana K, Epstein D, Jefferyes S, Qidwai U, Aftab Z, Mujeeb I, Snead D, Rajpoot N (2017) Glandular morphometrics for objective grading of colorectal adenocarcinoma histology images. Scientific Reports 7(1):1–12

    Article  Google Scholar 

  2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a Cancer Journal for Clinicians 68 (6):394–424

    Google Scholar 

  3. Canny J (1986) A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6):679–698

    Article  Google Scholar 

  4. Dif N, Elberrichi Z (2020) A new deep learning model selection method for colorectal cancer classification. International Journal of Swarm Intelligence Research (IJSIR) 11(3):72–88

    Article  Google Scholar 

  5. Egger J, Gsxaner C, Pepe A, Li J (2020) Medical deep learning–a systematic meta-review. arXiv:201014881

  6. Graham S, Chen H, Gamper J, Dou Q, Heng PA, Snead D, Tsang YW, Rajpoot N (2019) Mild-net: minimal information loss dilated network for gland instance segmentation in colon histology images. Med Image Anal 52:199–211

    Article  Google Scholar 

  7. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  8. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  9. Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med Image Anal 36:61–78

    Article  Google Scholar 

  10. Kim M, Yun J, Cho Y, Shin K, Jang R, Hj Bae, Kim N (2020) Deep learning in medical imaging. Neurospine 17(2):471

    Article  Google Scholar 

  11. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436 . https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  12. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  13. Lundervold AS, Lundervold A (2019) An overview of deep learning in medical imaging focusing on mri. Zeitschrift Für Medizinische Physik 29 (2):102–127

    Article  Google Scholar 

  14. Ponzio F, Macii E, Ficarra E, Di Cataldo S (2018) Colorectal cancer classification using deep convolutional networks. In: Proceedings of the 11th international joint conference on biomedical engineering systems and technologies, vol 2, pp 58–66

  15. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:14091556

  16. Sudana O, Gunaya IW, Putra IKGD (2020) Handwriting identification using deep convolutional neural network method. Telkomnika 18(4):1934–1941

    Article  Google Scholar 

  17. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  18. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  19. Torrey L, Shavlik J (2010) Transfer learning. In: Handbook of research on machine learning applications and trends: algorithms, methods, and techniques. IGI Global, pp 242–264

  20. Tou JT, Gonzalez RC (1974) Pattern recognition principles. prp

  21. Wu N, Phang J, Park J, Shen Y, Huang Z, Zorin M, Jastrzkebski S, Févry T, Katsnelson J, Kim E, et al. (2019) Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Transactions on Medical Imaging

  22. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921–2929

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Acknowledgements

This work was supported by the GRRC program of Gyeonggi province. [GRRC KGU 2020-B04, Image/Network-based Intellectual Information Manufacturing Service Research]

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    Authors and Affiliations

    1. Division of Computer Science and Engineering, Kyonggi University, Suwon, Republic of Korea

      Sang-Hyun Kim & Byoung-Dai Lee

    2. Department of Pathology, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea

      Hyun Min Koh

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    1. Sang-Hyun Kim
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    Correspondence to Byoung-Dai Lee.

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    Sang-Hyun Kim and Hyun Min Koh have contributed equally in this article as first authors.

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    Kim, SH., Koh, H.M. & Lee, BD. Classification of colorectal cancer in histological images using deep neural networks: an investigation. Multimed Tools Appl 80, 35941–35953 (2021). https://doi.org/10.1007/s11042-021-10551-6

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    • DOI: https://doi.org/10.1007/s11042-021-10551-6

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