Description

This paper introduces a novel, reliable, hybrid, two-stage method for bone x-ray classification and abnormality detection. The proposed method considers bones from seven areas of the upper extremities: namely, shoulder, humerus, forearm, elbow, wrist, hand, and finger. The novelty of the proposed work is not only in the application, but also in the proposed method itself by combining GNG network and eight models built from scratch and inspired from VGG model to achieve best performance and least computations possible. The features extracted from GNG are fed into a two-stage classification step. The first stage classifies a bone X-ray into one of seven types, after which it is directed according to type to one of seven classifiers trained to detect bone abnormality. Hence, the classification step consists of eight different models: one for classification and seven for abnormality detection. Experiments have been carried out using the MURA database, the largest public dataset of bone x-ray images. The best average sensitivity and specificity obtained for the first stage is 95.86% and 99.63%, respectively. For the second stage, the best average sensitivity and specificity obtained is 92.50% and 92.12%, respectively. These results are superior compared to state of art pretrained models. In addition, the computation and processing time are significantly decreased by the proposed scheme. Furthermore, to the best knowledge of authors, the proposed method is the first to integrate seven bones together in the same scheme. Finally, the hierarchical nature of the proposed method allows considering two problems together: bone classification and abnormality detection.