TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Papers/Classification of Shoulder X-Ray Images with Deep Learning...

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

Fatih Uysal, Fırat Hardalaç, Ozan Peker, Tolga Tolunay, Nil Tokgöz

2021-01-31Image ClassificationEnsemble LearningTransfer LearningDeep LearningGeneral Classification
PaperPDF

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.

Results

TaskDatasetMetricValueModel
Image ClassificationFracture/Normal Shoulder Bone X-ray Images on MURACohen’s Kappa score 0.6942Our Ensemble Learning-2
Image ClassificationFracture/Normal Shoulder Bone X-ray Images on MURAAUC score0.8862Our Ensemble Learning-1

Related Papers

Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations2025-07-18RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction2025-07-18Adversarial attacks to image classification systems using evolutionary algorithms2025-07-17Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy2025-07-17Federated Learning for Commercial Image Sources2025-07-17MUPAX: Multidimensional Problem Agnostic eXplainable AI2025-07-17Simulate, Refocus and Ensemble: An Attention-Refocusing Scheme for Domain Generalization2025-07-17Disentangling coincident cell events using deep transfer learning and compressive sensing2025-07-17