Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks

With the current COVID19 pandemic, we have to weigh human life, prosperity, and value, while implicitly acknowledging that controlling case spread and mortality is a challenge. Identifying COVID19-infected patients and disconnecting them to avoid COVID transmission is one of the most difficult tasks for clinicians. As a result, figuring out who infected with covid19 is crucial. COVID19 is identified using a 4-6-hour reverse transcription-polymerase chain reaction (RT-PCR). Another way to detect Coronavirus early in the disease process is by using chest X-rays (CXR).We extracted characteristics from chest X-ray images using VGG16 and ResNet50 deep learning algorithms, then classified them into three groups: viral pneumonia, normal, and COVID19. We ran 15,153 images through the models to see how accurate they were in real-world situations. For detecting COVID19 cases, the VGG16 model has an average accuracy of 89.34 %, whereas ResNet50 has an accuracy of 91.39 %. When utilizing deep learning to identify COVID19, however, a larger dataset is necessary. It has the desired effect of detecting situations accurately. Copyright (C) 2022 Elsevier Ltd. All rights reserved.