COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images

,e COVID-19 pandemic has a significant negative effect on people’s health, as well as on the world’s economy. Polymerase chain
reaction (PCR) is one of the main tests used to detect COVID-19 infection. However, it is expensive, time-consuming, and lacks
sufficient accuracy. In recent years, convolutional neural networks have grabbed many researchers’ attention in the machine
learning field, due to its high diagnosis accuracy, especially the medical image recognition. Many architectures such as Inception,
ResNet, DenseNet, and VGG16 have been proposed and gained an excellent performance at a low computational cost. Moreover,
in a way to accelerate the training of these traditional architectures, residual connections are combined with inception architecture. ,erefore, many hybrid architectures such as Inception-ResNetV2 are further introduced. ,is paper proposes an
enhanced Inception-ResNetV2 deep learning model that can diagnose chest X-ray (CXR) scans with high accuracy. Besides, a
Grad-CAM algorithm is used to enhance the visualization of the infected regions of the lungs in CXR images. Compared with
state-of-the-art methods, our proposed paper proves superiority in terms of accuracy, recall, precision, and F1-measure.