  Recently, researchers, specialists, and companies around the world are
rolling out deep learning and image processing-based systems that can fastly
process hundreds of X-Ray and computed tomography (CT) images to accelerate the
diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment.
Medical images analysis is one of the most promising research areas, it
provides facilities for diagnosis and making decisions of a number of diseases
such as MERS, COVID-19. In this paper, we present a comparison of recent Deep
Convolutional Neural Network (DCNN) architectures for automatic binary
classification of pneumonia images based fined tuned versions of (VGG16, VGG19,
DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and
Xception). The proposed work has been tested using chest X-Ray & CT dataset
which contains 5856 images (4273 pneumonia and 1583 normal). As result we can
conclude that fine-tuned version of Resnet50, MobileNet_V2 and
Inception_Resnet_V2 show highly satisfactory performance with rate of increase
in training and validation accuracy (more than 96% of accuracy). Unlike CNN,
Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance
(more than 84% accuracy).
