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A survey of feature extraction and fusion of deep learning for detection of abnormalities in video endoscopy of gastrointestinal-tract

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Abstract

A standard screening procedure involves video endoscopy of the Gastrointestinal tract. It is a less invasive method which is practiced for early diagnosis of gastric diseases. Manual inspection of a large number of gastric frames is an exhaustive, time-consuming task, and requires expertise. Conversely, several computer-aided diagnosis systems have been proposed by researchers to cope with the dilemma of manual inspection of the massive volume of frames. This article gives an overview of different available alternatives for automated inspection, detection, and classification of various GI abnormalities. Also, this work elaborates techniques associated with content-based image retrieval and automated systems for summarizing endoscopic procedures. In this survey, we perform a comprehensive review of feature extraction techniques and deep learning methods which were specifically developed for automatic analysis of endoscopic videos. In addition, we categorize features extraction techniques according to image processing domains and further we classify them based on their visual descriptions. We also review hybrid feature extraction techniques which are developed by the fusion of different kind of basic descriptors. Moreover, this survey covers various endoscopy data-sets available for the bench-marking of vision based algorithms. On the basis of literature, we explain emerging trends in computerized analysis of endoscopy. We also survey important issues, challenges, and future research directions to the development of computer-assisted systems for detection of maladies and interactive surgery in the GI tract.

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Notes

  1. http://www.cancer.net/navigating-cancer-care/diagnosing-cancer/tests-and-procedures/types-endoscopy.

  2. https://aidasub-chromogastro.grand-challenge.org.

  3. https://aidasub-cleceliachy.grand-challenge.org/description/.

  4. https://aidasub-clebarrett.grand-challenge.org.

  5. https://polyp.grand-challenge.org/site/Polyp/CVCClinicDB/.

  6. https://polyp.grand-challenge.org/site/Polyp/EtisLarib/.

  7. https://polyp.grand-challenge.org/site/Polyp/AsuMayo/.

  8. https://endovissub-abnormal.grand-challenge.org/data/.

  9. https://endovissub-barrett.grand-challenge.org/data_description/.

  10. https://endovissub2017-giana.grand-challenge.org/polypdetection.

  11. https://endovissub2017-giana.grand-challenge.org/polypsegmentation/.

  12. https://endovissub2017-giana.grand-challenge.org/polypsegmentation/.

  13. https://endovissub-instrument.grand-challenge.org/data/.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan

    Hussam Ali, Muhammad Sharif & Mussarat Yasmin

  2. Department of Computer Science, Cork Institute of Technology, Cork, Ireland

    Mubashir Husain Rehmani

  3. Department of Computer & Software Engineering, CE&ME, National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Farhan Riaz

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  1. Hussam Ali
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  2. Muhammad Sharif
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  3. Mussarat Yasmin
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  4. Mubashir Husain Rehmani
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  5. Farhan Riaz
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Correspondence to Hussam Ali.

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Ali, H., Sharif, M., Yasmin, M. et al. A survey of feature extraction and fusion of deep learning for detection of abnormalities in video endoscopy of gastrointestinal-tract. Artif Intell Rev 53, 2635–2707 (2020). https://doi.org/10.1007/s10462-019-09743-2

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