A CNN-Based Disease Detection Framework for Wireless Capsule Endoscopy Videos

In colonoscopy, wireless capsule endoscopy (WCE) is widely used since it is more physically friendly for patients than standard endoscopy. WCE is a low-risk and effective clinical operation for the small intestine endoscopy, which is less accessible to standard endoscopy. However, reviewing WCE videos can be challenging as it is time-consuming and requires considerable expertise. WCE videos are usually captured at a low resolution and partial frames are filtered out due to hardware limitations. Additional challenges arise from the diversity and complexity of gastrointestinal (GI) diseases. Moreover, inadequate clinic attention can cause clinical errors. Consequently, physicians are often overburdened with work. This paper presents a convolutional neutral networks (CNN) based framework to assist clinicians to review the WCE videos. The framework combines both image classification and object detection results to provide comprehensive results for disease detection in WCE videos. For disease classification, ResNet-50 was selected when experiments were conducted on the dataset. For disease detection, YOLO-X is employed on the images labelled with bonding boxes. Enhanced with an offline hard example mining (offline-HEM) procedure and fine-tuning on hyper-parameters, this framework can achieve high sensitivity for disease instances while maintaining acceptable specificity for false positives in video tests.