Abstract
The 2019 coronavirus pandemic (COVID-19) struck without warning, and existing medical screening and clinical management systems were unprepared, causing a high fatality rate. Given the virus's ongoing evolution, there is still a potential for reemergence; earlier weak preparedness will not be accepted in such a situation. Therefore, it is vital to understand and rectify past diagnostic work's flaws. RT-PCR and antigen tests, both widely used, have experienced problems in the past. They either were too sluggish or produced an excessive number of false negatives. Another issue was a lack of test kits. As a result, chest X-ray image-based disease classification has emerged. However, managing a variety of chest X-ray pictures for COVID-19 and pneumonia patients is complicated and error-prone. As a result, the only way to improve the current diagnosis is to apply deep learning algorithms that learn from radiography pictures and anticipate COVID-19 development. We constructed our own convolutional neural network (CNN) by incorporating transfer learning from the most popular ResNet, VGG, and InceptionNet models. The endeavor necessitated the creation of a sizable dataset that accurately depicted the patient population. Before importing the model, the images were enhanced to remove artifacts caused by noise, motion, or blurring that could impair the detection of infection. Preprocessing has a substantial impact on the model's accuracy. The results indicated that the VGG16 architecture, with a detection accuracy of 95.29%, is optimal for COVID-19 identification from X-ray images. Furthermore, most generated models outperformed current state-of-the-art research in the same field.