Computational Intelligence Method for Bones Classification and Abnormality Detection using X-ray Images

Abstract

Wrong diagnosis for bone abnormalities may lead to serious side effects. Moreover, exhausted, and over loaded doctors may miss some cases. Hence, Computer aided diagnosis systems have a vital role nowadays. Based on the conducted comparative analysis: 1) There is a lack of published datasets that can be used as benchmark due to the difficulty of collecting data from hospitals; 2) Most of the previous studies consider only one bone due to the high variability in the shape of different bone types and also due to lack of data; 3) Most of the existing studies don’t consider the abnormality type; 4) Most of the previous studies apply the traditional methods for feature extraction and classification, except for few new studies that utilize deep learning models (CNN models); 5) The models used in deep learning based studies are of huge depth which increases the training time and computation. Hence, a computer-aided diagnosis (CAD) system based on deep learning approach is proposed to consider the drawbacks of the literature. Bones of the upper extremities: namely, shoulder, humerus, forearm, elbow, wrist, hand, and finger are considered. All experiments have been carried out using the MURA database, the largest public dataset of