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Modified residual attention network for abnormalities segmentation and detection in WCE images

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Abstract

Wireless capsule endoscopy (WCE) is a technology that uses a pill-sized camera to visualize images of the digestive tract. It presents several advantages, since it is far less invasive, does not require sedation and has less potential complications compared to standard endoscopy. Hence, it might be exploited as alternative to the standard procedure. WCE is used to diagnosis a variety of gastro-intestinal diseases such as polyps, ulcers, Crohn’s disease and hemorrhages. Nevertheless, WCE videos can contain thousands of images per patient that must be screened by medical specialists, besides, the capsule free mobility and technological limits cause production of a low quality images. In this paper, a Nouvel method based on Dense-UNet deep learning segmentation model is presented. This approach aims at red lesion, ulcer and polyp detection from WCE images. Then, we propose a modified residual attention network for images classification. The proposed methods training and validation accuracies are 97.57% and 92.70%, with training and validation intersection over union and dice coefficients of 75.31%, 71.29%, 83.50% and 80.66%, respectively, in the red lesion dataset. In the polyp dataset, the method achieved a training and validation accuracies of 98.26% and 92.33%, with training and validation intersection over union and dice coefficients of 92.09%, 95.62%, 80.13% and 87.14%, respectively. Besides, the proposed architecture reached an average accuracy of 99.30%, sensitivity, specificity and F1 score of 98.61%, 100% and 99.30%, respectively. These results demonstrate that the proposed approach is satisfactory in polyp and red lesion segmentation and ulcer detection.

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Data availability

The first dataset analyzed during the current study is available in [https://rdm.inesctec.pt/dataset/nis-2018-003]. The second dataset analyzed during the current study is available publicly in [https://datasetninja.com/cvc-612]. The third dataset analyzed during the current study will be made available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the Ministry of Higher Education, Scientific Research, and Innovation (MHESRI), The Ministry of Industry, Trade and Green and Digital Economy (MITGDE), Digital Development Agency (DDA) and National Center for Scientific and Technical Research (NCSTR). Project number: ALKHAWARIZMI/2020/20.

Funding

This work was funded by the Ministry of Higher Education, Scientific Research, and Innovation (MHESRI), The Ministry of Industry, Trade and Green and Digital Economy (MITGDE), Digital Development Agency (DDA) and National Center for Scientific and Technical Research (NCSTR) (ALKHAWARIZMI/2020/20).

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Authors and Affiliations

  1. LabSIV, Department of Computer Science, Faculty of Science, Ibn Zohr University, BP 8106, 80000, Agadir, Morocco

    Said Charfi, Mohamed El Ansari & Lahcen Koutti

  2. Informatics and Applications Laboratory, Department of Computer Science, Faculty of Science, My Ismail University, Meknes, Morocco

    Mohamed El Ansari

  3. LabSIE, Department of Mathematics and Computer Science, Multidisciplinary Faculty, Ibn Zohr University, BP 638, 45000, Ouarzazate, Morocco

    Ayoub Ellahyani & Ilyas Eljaafari

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  1. Said Charfi
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  2. Mohamed El Ansari
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  4. Ayoub Ellahyani
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Correspondence to Said Charfi.

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Charfi, S., Ansari, M.E., Koutti, L. et al. Modified residual attention network for abnormalities segmentation and detection in WCE images. Soft Comput (2024). https://doi.org/10.1007/s00500-023-09576-w

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