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Musculoskeletal abnormalities along with bone fractures are a wide range of
abnormalities that account for most visits of patients to Emergency department of hospitals.
According to an estimate, more than 1.7 billion people are affected by musculoskeletal disorders
each year. Bone X-rays are the first line imaging modality for imaging of fractured bones.
Radiologists then undergo reporting of X-rays for detection of fractures and pathologies.
Classification of bone X-rays into normal and abnormal is a time-taking process and is also
subjected to variability between different radiologists. Therefore, the use of automatic classifiers
incorporating deep learning algorithms is currently in use in clinical diagnostics. MURA is a
large publicly available dataset released by the machine learning group of Stanford university.
MURA dataset consists of 40,895 multi-view images of upper limb that belong to seven regions
namely shoulder, humerus, elbow, forearm, wrist, hand, and fingers. In this study we propose the
use of the single DenseNet-169 model trained on complete dataset along with multiple preprocessing and data augmentation steps, based on Keras in TensorFlow. Training data was
divided into 80:20 for training and validation respectively, whereas, testing of model was done
on validation set. The results obtained through the proposed technique include 80% testing
accuracy. This validates the effectiveness of this method for bone fractures classification.