Kashin-Beck disease diagnosis based on deep learning from hand X-ray images.

• An automated Kashin Beck Disease (KBD) diagnosis algorithm was proposed based on deep learning. • The method focuses on multi-features fusion for detection. The features are extracted from hand X-ray images by DCNN, including global and local features. • Global features represent structures of hand bones, and local features represent subtle edge information from critical regions of metaphysis based on the domain knowledge of KBD. • The model can effectively diagnose KBD and provide substantial benefits to reduce largescale screening costs and missed diagnosis rate. Kashin-Beck Disease (KBD) is a serious endemic bone disease leading to short stature. The early radiological examinations are crucial for potential patients. However, many children in rural China cannot be diagnosed in time due to the shortage of professional orthopedists. In this paper, an algorithm is developed to automatically screening KBD based on hand X-ray images of subjects, which can help the government reducing human resources investment and assisting the poor precisely. The KBD diagnosis method focuses on multi-feature fusion for classification. Two kinds of features presented in X-ray images are extracted by a deep convolutional neural network (DCNN). One is the global features that represent shapes and structures of the whole hand bone. The other is local features that represent edge and texture information from critical regions of the metaphysis. The global features tend to sketch the major informative parts, whereas other fine local features can provide supplementary information. Then both kinds of features are combined and fed into the KBD classifier of a fully connected neural network (FCNN) to obtain diagnostic results. Our research team collected 960 samples in KBD endemic areas of Tibet from 2017 to 2018. The dataset contains 219 KBD positive images and 741 negative images. Experiments indicate that the method based on multi-feature achieves the best average accuracy and sensitivity rate of of 98.5% and 97.6% for diagnosis, which is 4.0% and 7.6% higher than the method with only the global features respectively. The KBD diagnosis method shows that our proposed multi-feature fusion helps to achieve higher diagnosis performance and stability compared with only using global features for detection. The automated KBD diagnosis algorithm provides substantial benefits to reduce large-scale screening costs and missed diagnosis rate. [ABSTRACT FROM AUTHOR]