Deep Learning for COVID-19 Diagnosis Using Pretrained and Non-Pretrained Models

Abstract

This article proposes a deep-learning approach to classify COVID-19 cases using image data. Our model uses a convolutional neural network (CNN) to extract features from chest X-rays and classify them as positive or negative for COVID-19. A COVID-19 case dataset is compared to traditional machine learning methods to evaluate model performance. The results obtained demonstrate the effectiveness of the deep learning model in accurately detecting COVID-19 cases with an overall accuracy of 96%. This approach is helpful for rapid and automated diagnosis of COVID-19, especially in resource-limited settings. The proposed method yielded remarkable results compared with recent results.