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Accurate small bowel lesions detection in wireless capsule endoscopy images using deep recurrent attention neural network
Abstract : Wireless capsule endoscopy (WCE) allows medical doctors to examine the interior of the small intestine with a non-invasive procedure. This methodology is particularly important for Crohn's disease (CD), where an early diagnosis improves treatment outcomes. However, the viewing and evaluation of WCE videos is a time-consuming process for the medical experts. In this work, we present a recurrent attention neural network for the detection in WCE images of CD lesions in the small bowel. Our classifier reaches 90.85% accuracy on our own dataset annotated by experts from the Hospital of Nantes. The model has also been tested on a public endoscopic dataset, the CAD-CAP database used for the GIANA competition, and achieves high performance on detection task with an accuracy of 99,67%. This automatic lesion classifier will greatly reduce the amount of time spent by gastroenterologists in reviewing WCE videos, which will likely foster the development of this technique and speed-up the diagnosis of CD.
Cited literature [18 references]
https://hal.archives-ouvertes.fr/hal-02296282
Contributor : Harold Mouchère Connect in order to contact the contributor
Submitted on : Wednesday, September 25, 2019 - 9:54:36 AM
Last modification on : Wednesday, April 27, 2022 - 4:20:55 AM
Long-term archiving on: : Sunday, February 9, 2020 - 9:37:28 PM
Contributor : Harold Mouchère Connect in order to contact the contributor
Submitted on : Wednesday, September 25, 2019 - 9:54:36 AM
Last modification on : Wednesday, April 27, 2022 - 4:20:55 AM
Long-term archiving on: : Sunday, February 9, 2020 - 9:37:28 PM
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