Classification of X-Ray Images of the Chest Using Convolutional Neural Networks

A proven way to detect various injuries: from fractures to heart failure, is an X-ray. However, because this examination method depends on the doctor's visual analysis, it can lead to misdiagnosis, that is, the case when the early stage of pneumonia will not be recognized and treatment will be ineffective. This study proposes using a convolutional neural network to classify chest X-rays to solve this problem. To do this, we analyzed the materials on the classification using neural networks for different areas of computer vision. In particular, convolutional neural networks for medical use are considered. The classification model of images on a database that included 112 thousand captions and 30 thousand unique patients is trained. High accuracy values of 0.93 and completeness of 0.99 models were obtained. An analysis of the literature on the acceleration, parallelism, and synchronization of convolutional neural networks was performed. Their shortcomings are taken into account, and a new optimization approach is proposed. The classification results were compared with a parallel approach on a GPU and a sequential on a CPU. The model trains on the GPU is 6.13 times faster than on the CPU based on the proposed algorithm.