Published online Jan 14, 2024. doi: 10.3748/wjg.v30.i2.170
Peer-review started: November 8, 2023
First decision: December 7, 2023
Revised: December 15, 2023
Accepted: December 26, 2023
Article in press: December 26, 2023
Published online: January 14, 2024
Deep learning provides an efficient automatic image recognition method for small bowel (SB) capsule endoscopy (CE) that can assist physicians in diagnosis. How
To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks, and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.
The proposed model represents a two-stage method that combined image classification with object detection. First, we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images, normal SB mucosa images, and invalid images. Then, the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding, and the location of the lesion was marked. We constructed training and testing sets and compared model-assisted reading with physician reading.
The accuracy of the model constructed in this study reached 98.96%, which was higher than the accuracy of other systems using only a single module. The sen
The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images, which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.
Core Tip: In clinical practice, capsule endoscopy is often used to detect small bowel (SB) lesions and find the cause of bleeding. Here, we have proposed a classification and detection model to automatically identify various SB lesions and their bleeding risks, and label the lesions accurately. This model can enhance the diagnostic efficiency of physicians and improve the ability of physicians to identify high-risk bleeding groups.