Effectively classifying medical images play an essential role in aiding clinical care and treatment. For example, Analysis X-ray is the best approach to diagnose pneumonia [1] which causes about 50,000 people to die per year in the US [2], but classifying pneumonia from chest X-rays needs professional radiologists which is a rare and expensive resource for some regions. The use of the traditional machine learning methods, such as support vector methods (SVMs), in medical image classification, began long ago. However, these methods

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Everyone’s life on earth influenced by a global coronavirus outbreak COVID- 19. Two regular practices, pathology tests, and Computer Tomography (CT) scan used to diagnose COVID-19. Pathology tests produce a considerable amount of false-positives & are time-consuming, whereas CT scans tests are costly and require expert advice. Hence, the main aim of this work is to develop a fast, accurate, and low-cost diagnostic system for detection of COVID-19 using inexpensive chest X-rays and the modern Deep Convolutional Neural Network(CNN) approach to assist medical professionals. In this study, two pre-trained CNN models (VGG16 and InceptionV3) are evaluated by several experiments using data augmentations. The analysis is based on 2905 images of chest X-rays with 219 confirmed positive COVID-19 and 1345 positive pneumonia cases taken from the open-source database consisting of patients suffering from the COVID-19 disease. Since a database consists of multiple types of diseases, multiclass classification for diagnosis of COVID-19 is used. The InceptionV3 model provides the highest classification accuracy (99.35% and 98.29%) for two binary classifications (normal vs. COVID-19 and COVID- 19 vs. Pneumonia) compare to VGG16 model’s accuracy (97.71% and 96.27%). Whereas, VGG16 provides highest accuracy (98.84%)for multiclass-classification(normal vs COVID- 19 vs pneumonia) as compared to VGG16 model’s accuracy(96.35%).

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