Innovative and efficient CNN structure works for chest X-ray images classification

ABSTRACT

In the era of global transmission of COVID-19, it is a challenge for physicians to efficiently and accurately use chest Xray images to diagnose whether a patient is positive or not. The application of deep learning and computer vision in medical image processing solves this problem, but a highly accurate method is still needed. In this research, we proposed an innovative CNN structure used for chest X-ray classification. Based on deep learning and CNN, this new architecture has an efficient training process and the performance of accuracy is better than other classic nets. The best accuracy on the test dataset is 97.68%. It has competitive advantages over AlexNet, LeNet-5, and Vgg-16. Dropout, Data augmentation, and Grad-CAM technique are added to improve performance. © 2022 IEEE.