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arXiv · 2504.08659

BowelRCNN: Region-based Convolutional Neural Network System for Bowel Sound Auscultation

Igor Matynia·Robert Nowak
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Abstract

Sound events representing intestinal activity detection is a diagnostic tool with potential to identify gastrointestinal conditions. This article introduces BowelRCNN, a novel bowel sound detection system that uses audio recording, spectrogram analysys and region-based convolutional neural network (RCNN) architecture. The system was trained and validated on a real recording dataset gathered from 19 patients, comprising 60 minutes of prepared and annotated audio data. BowelRCNN achieved a classification accuracy of 96% and an F1 score of 71%. This research highlights the feasibility of using CNN architectures for bowel sound auscultation, achieving results comparable to those of recurrent-convolutional methods.

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Igor Matynia, Robert Nowak. 2025-04-11. BowelRCNN: Region-based Convolutional Neural Network System for Bowel Sound Auscultation. https://arxiv.org/abs/2504.08659

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