Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning
This tool helps radiologists by automating detection in bone X-rays, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of automating abnormality detection in musculoskeletal radiographs using a deep learning tool called MuRAD, which achieves an F1 score of 0.822 and a Cohen's kappa of 0.699, comparable to expert radiologists.
This paper introduces MuRAD (Musculoskeletal Radiograph Abnormality Detection tool), a tool that can help radiologists automate the detection of abnormalities in musculoskeletal radiographs (bone X-rays). MuRAD utilizes a Convolutional Neural Network (CNN) that can accurately predict whether a bone X-ray is abnormal, and leverages Class Activation Map (CAM) to localize the abnormality in the image. MuRAD achieves an F1 score of 0.822 and a Cohen's kappa of 0.699, which is comparable to the performance of expert radiologists.