Detection of Multiple Lesion Candidates on Capsule Endoscopy Images by Learning Multiple Lesions using YOLOv5

Abstract

The Capsule endoscopy is a technique to capture images of the inside of the gastrointestinal tract by swallowing a device measuring approximately 11 mm in diameter and 26 mm in length. Compared with conventional endoscopy, capsule endoscopy is less burdensome on patients while allowing observations of the small intestine. This non-invasive technique produces more than 50,000 images in a single examination. Because a physician must visually check each image, a diagnosis is time consuming and labor intensive.

This study investigated automatic detection of lesions to reduce the burden on physicians, preventing missed lesions and support diagnosis.

Here, we use YOLOv5 (You Only Look Once version 5), which is a general object detection model, to automatically detect lesions after training a model with 3 types of lesion images. When the recall was 100% to ensure that no lesion was missed, the polyp accuracy, ulcer accuracy, Type A accuracy and Type B accuracy were 96%, 99%, 77% and 94%, respectively. In the future, we will train the model with additional images of other lesions and improve the precision rate.