Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning

by   Goodarz Mehr, et al.
Virginia Polytechnic Institute and State University

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.


Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge

This work presents a mitosis detection method with only one vanilla Conv...

FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection

To test the possibility of differentiating chest x-ray images of COVID-1...

DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation

Image spam emails are often used to evade text-based spam filters that d...

Deep learning based cough detection camera using enhanced features

Coughing is a typical symptom of COVID-19. To detect and localize coughi...

A Deep Transfer Learning Framework for Seismic Data Analysis: A Case Study on Bright Spot Detection

Bright spots, strong indicators of the existence of hydrocarbon accumula...

Please sign up or login with your details

Forgot password? Click here to reset