X-ray chest image classification by a small-sized convolutional neural network

Convolutional Neural Networks are widely used in image classification problems due to their high performances. Deep learning methods are also used recently in the classification of medical signals or images. It is observed that well-known pre-trained large networks are used in the classification of X-ray chest images. The performances of these networks on the training set are satisfactory, but their practical use includes some difficulties. The usage of the different imaging modalities in the training process decreases the generalization ability of these networks. And also, due to their large sizes, they are not suitable for real-time applications. In this study, new network structures and the size of the input image are investigated for the classification of Xray chest images. It is observed that chest images are assigned to twelve classes with approximately 86% success rate by using the proposed network, and the training is carried out in a short time due to the small network structure. The proposed network is run as a real time application on an embedded system with a camera and it is observed that the classification result is produced in less than one second.