Smart Video Capsule Endoscopy: Raw Image-Based Localization for Enhanced GI Tract Investigation
Oliver Bause, Julia Werner, Paul Palomero Bernardo, Oliver Bringmann

TL;DR
This paper presents an energy-efficient, raw image-based AI approach for medical capsule endoscopy, enabling accurate organ classification with minimal energy consumption on resource-limited edge devices.
Contribution
It introduces a lightweight CNN model and raw Bayer image processing on a low-power hardware platform for improved GI tract investigation.
Findings
Achieved 93.06% accuracy on Bayer images.
Reduced energy consumption by 89.9% compared to traditional methods.
Implemented on a PULPissimo SoC with RISC-V core.
Abstract
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…
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