Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models

TL;DR

This study evaluates 26 deep learning models and develops ensemble methods to classify shoulder X-ray images as fracture or non-fracture, achieving high accuracy and AUC, aiding medical diagnosis.

Contribution

The paper introduces two ensemble deep learning models that improve shoulder fracture detection accuracy over individual pretrained models.

Findings

EL2 achieved highest accuracy and Cohen's kappa among models.

EL1 and EL2 models reached test accuracies of approximately 84.5% and 84.7%.

AUC scores for ensemble models were around 0.87 to 0.89.

Abstract

Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from Xradiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture / non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pretrained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pretrained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pretrained models with the best performance, test accuracy was 0.8455,0.8472, Cohens kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862,0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohens kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.