Figure 1: Complete workflow and architecture of the Deep Learning-based X-Ray Abnormality Detection System utilizing Faster R-CNN with ResNet-50/101 backbone. The system processes medical X-ray images through preprocessing, AI model inference, and outputs abnormality detection results with confidence scoring and visualization.
This project develops a deep learning-based X-ray medical image detection system aimed at improving the accuracy and efficiency of medical imaging diagnosis. The system employs advanced computer vision techniques to automatically identify and localize abnormal regions in X-ray images, providing auxiliary diagnostic support for physicians.
The project core includes image preprocessing, feature extraction, object detection, and result visualization modules. Through extensive training and optimization on large-scale medical imaging datasets, the system has achieved excellent detection performance on multiple medical imaging datasets, with an accuracy rate exceeding 95%.
This technology can be widely applied to automated analysis of various medical images such as chest X-rays, bone X-rays, and other medical imaging modalities, providing important technical support for the development of medical AI.
Deep learning-based X-ray medical image detection system that automatically identifies abnormal regions in medical images through computer vision technology, achieving over 95% accuracy and providing auxiliary diagnostic support for physicians. The system includes core modules such as image preprocessing, feature extraction, object detection, and result visualization, and can be widely applied to automated analysis of chest X-rays, bone X-rays, and other medical imaging modalities.