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Deep Neural Networks to Distinguish Between Crohn’s Disease and Ulcerative Colitis

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Pattern Recognition and Image Analysis (IbPRIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14062))

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Abstract

The number of patients with inflammatory bowel disease (IBD) has been increasing. The diagnosis is a difficult task for the gastroenterologist performing the endoscopic examination. However, in order to prescribe medical treatment and provide quality of life to the patient, the diagnosis must be quick and accurate. Therefore, it is important to develop tools that, based on the characteristics of the inflammation present in the mucosa, automatically recognise the type of inflammatory bowel disease. This paper presents a study where the objective was to collect and analyse endoscopic images referring to Crohn’s disease and Ulcerative colitis using six deep learning architectures: AlexNet, ResNet50, InceptionV3, VGG-16, ResNet50+MobileNetV2, and a hybrid model. The hybrid model consists of the combination of two architectures, a CNN and a LSTM. This work also presents techniques that can be used to pre-process the images before the training to remove accessory elements from the image. The obtained results demonstrate that it is possible to automate the process of diagnosing patients with IBD using convolutional networks for processing images collected during an endoscopic examination, and thus develop tools to help the medical specialist diagnose the disease.

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References

  1. Borgli, H., Riegler, M., Thambawita, V., Jha, D., Hicks, S., Halvorsen, P.: The HyperKvasir Dataset. OSF (2019)

    Google Scholar 

  2. Borgli, H., et al.: HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci. Data 7, 283 (2020)

    Article  Google Scholar 

  3. Cardoso, J.S., Sousa, R., Domingues, I.: Ordinal data classification using kernel discriminant analysis: a comparison of three approaches. In: 11th International Conference on Machine Learning and Applications. vol. 1, pp. 473–477 (2012)

    Google Scholar 

  4. Chicco, D., Jurman, G.: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21, 6 (2020)

    Article  Google Scholar 

  5. Chierici, M., et al.: Automatically detecting Crohn’s disease and ulcerative colitis from endoscopic imaging. BMC Med. Inf. Decis. Making 22, 300 (2022)

    Article  Google Scholar 

  6. CrohnIPI. https://crohnipi.ls2n.fr/en/crohn-ipi-project/. Accessed 21 Feb 2023

  7. Doença inflamatória do intestino CUF. https://www.cuf.pt/saude-a-z/doenca-inflamatoria-do-intestino. Accessed 21 Feb 2023

  8. Domingues, I., Cardoso, J.S.: Max-ordinal learning. IEEE Trans. Neural Netw. Learn. Syst. 25(7), 1384–1389 (2014)

    Article  Google Scholar 

  9. Dray, X., et al.: CAD-CAP: une base de données française à vocation internationale, pour le développement et la validation d’outils de diagnostic assisté par ordinateur en vidéocapsule endoscopique du grêle. In: Endoscopy, pp. s-0038-1623358 (2018)

    Google Scholar 

  10. Fatema, K., Montaha, S., Rony, M.A.H., Azam, S., Hasan, M.Z., Jonkman, M.: A robust framework combining image processing and deep learning hybrid model to classify cardiovascular diseases using a limited number of paper-based complex ECG images. Biomedicines 10, 2835 (2022)

    Article  Google Scholar 

  11. Ghouri, Y.A., Tahan, V., Shen, B.: Secondary causes of inflammatory bowel diseases. World J. Gastroenterol. 26, 3998–4017 (2020)

    Article  Google Scholar 

  12. Khan, M.N., Hasan, M.A., Anwar, S.: Improving the robustness of object detection through a multi-camera-based fusion algorithm using fuzzy logic. Front. Artif. Intell. 4, 638951 (2021)

    Article  Google Scholar 

  13. Maissin, A., et al.: Multi-expert annotation of Crohn’s disease images of the small bowel for automatic detection using a convolutional recurrent attention neural network. Endoscopy Int. Open 09, E1136–E1144 (2021)

    Article  Google Scholar 

  14. Polat, G., Kani, H.T., Ergenc, I., Alahdab, Y.O., Temizel, A., Atug, O.: Improving the computer-aided estimation of ulcerative colitis severity according to mayo endoscopic score by using regression-based deep learning. Inflamm. Bowel Dis., izac226 (2022)

    Google Scholar 

  15. Polat, G., Kani, H.T., Ergenc, I., Alahdab, Y.O., Temizel, A., Atug, O.: Labeled Images for Ulcerative Colitis (LIMUC) Dataset (2022)

    Google Scholar 

  16. Sairenji, T., Collins, K.L., Evans, D.V.: An update on inflammatory bowel disease. Prim. Care: Clin. Off. Pract. 44, 673–692 (2017)

    Article  Google Scholar 

  17. Seyedian, S.S., Nokhostin, F., Malamir, M.D.: A review of the diagnosis, prevention, and treatment methods of inflammatory bowel disease. J. Med. Life 12, 113–122 (2019)

    Article  Google Scholar 

  18. Shahzadi, I., Tang, T.B., Meriadeau, F., Quyyum, A.: CNN-LSTM: cascaded framework for brain tumour classification. In: IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 633–637 (2018)

    Google Scholar 

  19. Stidham, R.W., et al.: Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. JAMA Netw. Open 2(5), e193963 (2019)

    Article  Google Scholar 

  20. Turan, M., Durmus, F.: UC-NfNet: deep learning-enabled assessment of ulcerative colitis from colonoscopy images. Med. Image Anal. 82, 102587 (2022)

    Article  Google Scholar 

  21. Udristoiu, A.L., et al.: Deep learning algorithm for the confirmation of mucosal healing in Crohn’s disease, based on confocal laser endomicroscopy images. J. Gastroint. Liver Dis. 30, 59–65 (2021)

    Article  Google Scholar 

  22. Vallée, R., Coutrot, A., Normand, N., Mouchère, H.: Accurate small bowel lesions detection in wireless capsule endoscopy images using deep recurrent attention neural network. In: IEEE 21st International WS on Multimedia Signal Proceedings (MMSP) (2019)

    Google Scholar 

  23. Vallée, R., Coutrot, A., Normand, N., Mouchère, H.: Influence of expertise on human and machine visual attention in a medical image classification task. In: European Conference on Visual Perception (2021)

    Google Scholar 

  24. Vallée, R., Maissin, A., Coutrot, A., Mouchère, H., Bourreille, A., Normand, N.: CrohnIPI: an endoscopic image database for the evaluation of automatic Crohn’s disease lesions recognition algorithms. In: Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, p. 61. SPIE (2020)

    Google Scholar 

  25. Vankdothu, R., Hameed, M.A., Fatima, H.: A brain tumor identification and classification using deep learning based on CNN-LSTM method. Comput. Electr. Eng. 101, 107960 (2022)

    Article  Google Scholar 

  26. Wehkamp, J., Götz, M., Herrlinger, K., Steurer, W., Stange, E.F.: Inflammatory bowel disease: Crohn’s disease and ulcerative colitis. Deutsches Ärzteblatt Int. 113, 72 (2016)

    Google Scholar 

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Authors and Affiliations

  1. Instituto Politécnico de Coimbra, Instituto Superior de Engenharia, Rua Pedro Nunes - Quinta da Nora, 3030-199, Coimbra, Portugal

    José Maurício & Inês Domingues

  2. Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP): Grupo de Física Médica, Radiobiologia e Protecção Radiológica, Porto, Portugal

    Inês Domingues

Authors
  1. José Maurício
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  2. Inês Domingues
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Correspondence to Inês Domingues .

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Editors and Affiliations

  1. University of Alicante, Alicante, Spain

    Antonio Pertusa

  2. University of Alicante, Alicante, Spain

    Antonio Javier Gallego

  3. Universitat Politècnica de València, Valencia, Spain

    Joan Andreu Sánchez

  4. IPO Porto, Coimbra, Portugal

    Inês Domingues

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Maurício, J., Domingues, I. (2023). Deep Neural Networks to Distinguish Between Crohn’s Disease and Ulcerative Colitis. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_42

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  • DOI: https://doi.org/10.1007/978-3-031-36616-1_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36615-4

  • Online ISBN: 978-3-031-36616-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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