COVID-19 is an infectious disease that has invaded the world since 2019 starting from China. It is caused by a new member of the corona viruses' family that attacks the respiratory system of living body leading to fever, cough, general tiredness and to death in worst case scenarios. The disease is commonly detected by RT-PCR tests, which is time consuming and relatively expensive. The need for faster, cheaper, and more precise diagnostic tool raised the demand for the utilization of technology and artificial intelligence in this purpose. This study aims to build a robust deep learning algorithm using convolutional neural networks (CNNs) that is capable to classify chest X-ray images into COVID-19, viral pneumonia, and normal cases. X-ray is a safe and inexpensive imaging system that is available at all hospitals and healthcare centers. A novel CNN model built from scratch (COV-X) has been introduced and shown 94% accuracy in the classification purpose. VGG16, VGG19, RESNET50, XCEPTION, and MOBILENET are pre-Trained models that have shown an accuracy of 95%, 95%, 33%, 65%, and 77%, respectively. Findings prove that deep learning is an effective technique for early detection of COVID-19, it provides automatic detection with high reliability to help the healthcare professions and avoid the pandemic from spreading more. © 2021 IEEE.
Deep Learning Approach for COVID-19 Detection Based on X-Ray Images