Bone X-ray abnormality detection using MURA dataset

Abstract

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