Abstract
In this paper, we investigate anomaly detection in orthopedics musculoskeletal radiographs using deep learning. We examine thirteen models from the most powerful neural network families: generative adversarial networks (GANs), autoencoders (AEs) and convolutional neural network (CNN). The main goal is to detect anomalies in musculoskeletal radiographs using the Stanford Musculoskeletal Radiographs (MURA) dataset. The results of the examined models were compared to several recent studies. Generally, CNN models achieve the best score of 0.822 which is a very promising result, competitive for expert radiologists performance.
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Ounasser, N., Rhanoui, M., Mikram, M., El Asri, B. (2023). Anomaly Detection in Orthopedic Musculoskeletal Radiographs Using Deep Learning. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 693. Springer, Singapore. https://doi.org/10.1007/978-981-99-3243-6_8
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