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Deep neural network approaches for detecting gastric polyps in endoscopic images

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Abstract

Gastrointestinal endoscopy is the primary method used for the diagnosis and treatment of gastric polyps. The early detection and removal of polyps is vitally important in preventing cancer development. Many studies indicate that a high workload can contribute to misdiagnosing gastric polyps, even for experienced physicians. In this study, we aimed to establish a deep learning–based computer-aided diagnosis system for automatic gastric polyp detection. A private gastric polyp dataset was generated for this purpose consisting of 2195 endoscopic images and 3031 polyp labels. Retrospective gastrointestinal endoscopy data from the Karadeniz Technical University, Farabi Hospital, were used in the study. YOLOv4, CenterNet, EfficientNet, Cross Stage ResNext50-SPP, YOLOv3, YOLOv3-SPP, Single Shot Detection, and Faster Regional CNN deep learning models were implemented and assessed to determine the most efficient model for precancerous gastric polyp detection. The dataset was split 70% and 30% for training and testing all the implemented models. YOLOv4 was determined to be the most accurate model, with an 87.95% mean average precision. We also evaluated all the deep learning models using a public gastric polyp dataset as the test data. The results show that YOLOv4 has significant potential applicability in detecting gastric polyps and can be used effectively in gastrointestinal CAD systems.

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Gastric Polyp Detection Process using Deep Learning with Private Dataset

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Authors and Affiliations

  1. Faculty of Medicine, Department of Gastroenterology, Karadeniz Technical University, Trabzon, Turkey

    Serdar Durak & Murat Erkut

  2. Department of Geoinformatics, Yildiz Technical University, Istanbul, Turkey

    Bülent Bayram, Tolga Bakırman, Metehan Doğan, Mert Gürtürk & Burak Akpınar

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  1. Serdar Durak
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  2. Bülent Bayram
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  3. Tolga Bakırman
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Correspondence to Tolga Bakırman.

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All procedures performed in studies involving human participants was approved by the Head of Scientific Research Ethics Committee of Farabi Hospital, Faculty of Medicine, Karadeniz Technical University, Trabzon, Turkey, on 24.07.2020 with the protocol number 2020/165 and the document number 24237859–488. As this is a retrospective study formal consent is not required.

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Durak, S., Bayram, B., Bakırman, T. et al. Deep neural network approaches for detecting gastric polyps in endoscopic images. Med Biol Eng Comput 59, 1563–1574 (2021). https://doi.org/10.1007/s11517-021-02398-8

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  • DOI: https://doi.org/10.1007/s11517-021-02398-8

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