Smart Video Capsule Endoscopy: Raw Image-Based Localization for Enhanced GI Tract Investigation
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arXiv
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| Format: | Preprint |
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2025
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| author | Bause, Oliver Werner, Julia Bernardo, Paul Palomero Bringmann, Oliver |
| author_facet | Bause, Oliver Werner, Julia Bernardo, Paul Palomero Bringmann, Oliver |
| contents | For many real-world applications involving low-power sensor edge devices deep neural networks used for image classification might not be suitable. This is due to their typically large model size and require- ment of operations often exceeding the capabilities of such resource lim- ited devices. Furthermore, camera sensors usually capture images with a Bayer color filter applied, which are subsequently converted to RGB images that are commonly used for neural network training. However, on resource-constrained devices, such conversions demands their share of energy and optimally should be skipped if possible. This work ad- dresses the need for hardware-suitable AI targeting sensor edge devices by means of the Video Capsule Endoscopy, an important medical proce- dure for the investigation of the small intestine, which is strongly limited by its battery lifetime. Accurate organ classification is performed with a final accuracy of 93.06% evaluated directly on Bayer images involv- ing a CNN with only 63,000 parameters and time-series analysis in the form of Viterbi decoding. Finally, the process of capturing images with a camera and raw image processing is demonstrated with a customized PULPissimo System-on-Chip with a RISC-V core and an ultra-low power hardware accelerator providing an energy-efficient AI-based image clas- sification approach requiring just 5.31 μJ per image. As a result, it is possible to save an average of 89.9% of energy before entering the small intestine compared to classic video capsules. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23398 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Smart Video Capsule Endoscopy: Raw Image-Based Localization for Enhanced GI Tract Investigation Bause, Oliver Werner, Julia Bernardo, Paul Palomero Bringmann, Oliver Image and Video Processing Hardware Architecture Computer Vision and Pattern Recognition For many real-world applications involving low-power sensor edge devices deep neural networks used for image classification might not be suitable. This is due to their typically large model size and require- ment of operations often exceeding the capabilities of such resource lim- ited devices. Furthermore, camera sensors usually capture images with a Bayer color filter applied, which are subsequently converted to RGB images that are commonly used for neural network training. However, on resource-constrained devices, such conversions demands their share of energy and optimally should be skipped if possible. This work ad- dresses the need for hardware-suitable AI targeting sensor edge devices by means of the Video Capsule Endoscopy, an important medical proce- dure for the investigation of the small intestine, which is strongly limited by its battery lifetime. Accurate organ classification is performed with a final accuracy of 93.06% evaluated directly on Bayer images involv- ing a CNN with only 63,000 parameters and time-series analysis in the form of Viterbi decoding. Finally, the process of capturing images with a camera and raw image processing is demonstrated with a customized PULPissimo System-on-Chip with a RISC-V core and an ultra-low power hardware accelerator providing an energy-efficient AI-based image clas- sification approach requiring just 5.31 μJ per image. As a result, it is possible to save an average of 89.9% of energy before entering the small intestine compared to classic video capsules. |
| title | Smart Video Capsule Endoscopy: Raw Image-Based Localization for Enhanced GI Tract Investigation |
| topic | Image and Video Processing Hardware Architecture Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.23398 |