Medical X-ray Image Classification Method Based on Convolutional Neural Networks

Artificial intelligence and machine learning, including convolutional neural networks are increasingly entering the field of healthcare and medicine. The aim of the study is to optimize the learning process of convolutional neural networks through X-ray images pre-processing. A model for optimizing the overall architecture of a classifying convolutional neural network of chest X-rays by reducing the total number of convolutional operations is presented. The experimental results prove the successful application of the optimization process on the training of classification convolutional networks. There is a significant reduction in the training time of each epoch in the optimized convolutional networks. The optimization is of the order of 25% for the network with an input layer size of 124 x 124 and about 27% for the network with an input layer size of 122 x 122. The method can be applied in any field of image classification in which the informative image regions are grouped and subject to segmentation.