Deep Learning-based Multiple Bleeding Detection in Wireless Capsule Endoscopy

-Wireless Capsule Endoscopy (WCE) is a diagnostic technology for gastrointestinal tract pathology detection. It has emerged as an alternative to conventional endoscopy which could be distressing to the patient. However, the diagnosis process requires to view and analyze hundreds of frames extracted from WCE video. This makes the diagnosis tedious. For this purpose, researches related to the automatic detection of signs of gastrointestinal diseases have been boosted. In this paper, we design a pattern recognition system for detecting Multiple Bleeding Spots (MBS) using WCE video. The proposed system relies on the Deep Learning approach to accurately recognize multiple bleeding spots in the gastrointestinal tract. Specifically, the You Only Look Once (YOLO) Deep Learning models are explored in this paper, namely, YOLOv3, YOLOv4, YOLOv5 and YOLOv7. The results of experiments showed that YOLOv7 is the most appropriate model for designing the proposed MBS detection system. Specifically, the proposed system achieved a mAP of 0.86, and an IoU of 0.8. Moreover, the results of the detection were enhanced by augmenting the training data to reach a mAP of 0.883.