Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning

Abstract

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.

References in corpus (6)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
• MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
• Detecting hip fractures with radiologist-level performance using deep neural networks
• Abnormality Detection in Musculoskeletal Radiographs with Convolutional Neural Networks(Ensembles) and Performance Optimization