Assistance System for Real-time Polyp Detection Based on Convolutional Neural Network

Kvak, D. & Kvaková, K. Assistance system for real-time polyp detection based on convolutional neural network. Gastroenterologie a Hepatologie. ISSN 1804-803X. doi:10.48095/ccgh2021540.


The use of artificial intelligence as an assistive detection method in endoscopy has attracted increasing interest in recent years. Machine learning algorithms promise to improve the efficiency of polyp detection and even optical localization of findings, all with minimal training of the endoscopist. The practical goal of this study is to analyse the CAD software (computer-aided dia­gnosis) Carebot for colorectal polyp detection using a convolutional neural network. The proposed binary classifier for polyp detection achieves accuracy of up to 98%, specificity of 0.99 and precision of 0.96. At the same time, the need for the availability of large-scale clinical data for the development of artificial--intelligence-based models for the automatic detection of adenomas and benign neoplastic lesions is discussed.


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