BowelRCNN: Region-based Convolutional Neural Network System for Bowel Sound Auscultation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917982656004096 |
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| author | Matynia, Igor Nowak, Robert |
| author_facet | Matynia, Igor Nowak, Robert |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_08659 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BowelRCNN: Region-based Convolutional Neural Network System for Bowel Sound Auscultation Matynia, Igor Nowak, Robert Sound Audio and Speech Processing 68 J.3 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. |
| title | BowelRCNN: Region-based Convolutional Neural Network System for Bowel Sound Auscultation |
| topic | Sound Audio and Speech Processing 68 J.3 |
| url | https://arxiv.org/abs/2504.08659 |