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