Computer-Aided Detection (CAD) for COVID-19 based on Chest X-Ray Images using Convolutional Neural Network

Abstract

Covid-19 has spread throughout the world and has been declared as a pandemic by the World Health Organization (WHO). The disease was discovered by the end of 2019 in Wuhan-China. The number of deaths continues to surge sharply and spread to many countries. Covid-19 has sent billions of people on earth into lock-down when health services struggle to cope. A swift and reliable Covid-19 diagnosis system is needed, to direct the patient to the appropriate treatment and prevent the disease dissemination. During this time, we are familiar with rapid tests and Real-Time Polymerase Chain Reaction (RT-PCR) as the procedure of Covid-19 detection. Both of these procedures tend to be impractical and require specialized laboratories that are arranged in such away. It can also take several hours to wait for the amplification process until the results are known. In this study, we introduce a Covid-19 detection system based on Chest C-Ray images using Convolutional Neural Network (CNN). The dataset consists of 1000 images, 500 images each for positive Covid-19, and Pneumonia. The CNN model that was designed consisted of three hidden layers, a fully connected layer with sigmoid activation. The evaluation was conducted to determine the performance of the proposed model using matrices of precision, recall, F1, and accuracy. The experimental results show that the proposed method provides precision, recall, F1 was 1 and 100% accuracy, respectively. This research is expected to be tested in field validation, to help the medical authorities for clinical diagnosis.