Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning

• URL: http://arxiv.org/abs/2010.12030v1
• Date: Wed, 21 Oct 2020 01:48:56 GMT
• Title: Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning
• Authors: Goodarz Mehr
• Abstract summary: MuRAD is 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. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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