Related Experiment Video
Updated: Aug 29, 2025
07:47
Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication
Published on: December 15, 2023
784
Efficient Synchronous Real-Time CADe for Multicategory Lesions in Gastroscopy by Using Multiclass Detection Model
Yiji Ku1, Hui Ding1, Guangzhi Wang1
1Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.
Biomed Research International
|September 12, 2022
Summary
A new real-time computer-aided detection (CADe) system using YOLOv5 multiclass models efficiently detects multiple gastrointestinal lesions synchronously. This advanced CADe system offers high accuracy and speed for improved endoscopic examinations.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Gastroenterology
Background:
- Endoscopic examinations often require identifying multiple types of gastrointestinal lesions simultaneously.
- Existing computer-aided detection (CADe) systems may not efficiently handle multicategory lesion detection in real-time.
Purpose of the Study:
- To develop an efficient, synchronous, real-time CADe system for detecting multiple categories of gastrointestinal lesions.
- To compare a proposed multiclass YOLOv5 detection model against joint detection systems using single-class models.
Main Methods:
- A multiclass detection model based on YOLOv5 was developed for synchronous, real-time lesion detection.
- A retrospective dataset of 31,117 images from 3,747 patients was utilized.
- Online data augmentation and multiple loss functions were employed for model training.
Main Results:
- The proposed CADe system achieved 98% precision, 89% recall, and 90.2% mAP in detecting cancers, gastrointestinal stromal tumors, polyps, and ulcers.
- The system demonstrated a detection speed of 47 frames per second with 0.04s latency.
- The multiclass YOLOv5 system outperformed comparative joint detection systems in accuracy, speed, and latency.
Conclusions:
- The developed synchronous real-time CADe system with a multiclass detection model effectively detects multiple gastrointestinal lesions with high accuracy and efficiency.
- This system enhances the clinical application of CADe in endoscopy, offering a more efficient use of labeled medical images compared to multiple single-category models.
