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Musculoskeletal conditions affect more than 1.7 billion people worldwidebased on a study by Global Burden Disease, and they are the second greatestcause of disability[1,2]. The diagnosis of these conditions vary but mostlyphysical exams carried out and image tests. There are few imaging anddiagnostic experts while there is a huge workload of radiograph examinationswhich might affect diagnostic accuracy. We built machine learning models toperform abnormality detection using the available musculoskeletal publicdataset [3]. Convolutional Neural Networks (CNN) were used as are the mostsuccessful models in performing various tasks such as classification and objectdetection [4]. The development of the models involved theoretical study,iterative prototyping, and empirical evaluation of the results. The currentmodel, 169 layer DenseNet, by Pranav et al.(2018) on the abnormality detectiontask, the performance was lower than the worst radiologist in 5 out of the 7studies, and the overall model performance was lower than the best radiologist.We developed the ensemble200 model which scored 0.66 Cohen Kappa which waslower than the DenseNet model (Pranav et al, 2018) but the model performancewith the F1 score outperforms the DenseNet model and its Cohen Kappa scorevariability with the different studies is lower as the best cohen kappa scoreon the upper extremity studies is 0.7408 (Wrist) and the lowest is (0.5844)hand. The ensemble200 model outperformed DenseNet model on the finger studieswith a Cohen Kappa score of 0.653 showing reduced performance variability onthe model performance.

Abnormality Detection in Musculoskeletal Radiographs with Convolutional Neural Networks(Ensembles) and Performance Optimization