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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2310.07895 |
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| _version_ | 1866916727273553920 |
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| author | Werner, Julia Gerum, Christoph Reiber, Moritz Nick, Jörg Bringmann, Oliver |
| author_facet | Werner, Julia Gerum, Christoph Reiber, Moritz Nick, Jörg Bringmann, Oliver |
| contents | This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by exploring the combination of a Convolutional Neural Network (CNN) for classification with the time-series analysis properties of a Hidden Markov Model (HMM). It is demonstrated that successive time-series analysis identifies and corrects errors in the CNN output. Our approach achieves an accuracy of $98.04\%$ on the Rhode Island (RI) Gastroenterology dataset. This allows for precise localization within the gastrointestinal (GI) tract while requiring only approximately 1M parameters and thus, provides a method suitable for low power devices |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_07895 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs Werner, Julia Gerum, Christoph Reiber, Moritz Nick, Jörg Bringmann, Oliver Machine Learning This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by exploring the combination of a Convolutional Neural Network (CNN) for classification with the time-series analysis properties of a Hidden Markov Model (HMM). It is demonstrated that successive time-series analysis identifies and corrects errors in the CNN output. Our approach achieves an accuracy of $98.04\%$ on the Rhode Island (RI) Gastroenterology dataset. This allows for precise localization within the gastrointestinal (GI) tract while requiring only approximately 1M parameters and thus, provides a method suitable for low power devices |
| title | Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2310.07895 |