Deep Learning Medical Solutions: A short review on COVID-19 Detection Using Chest X-ray Image with CNN Multiple Architectures

The Deep learning techniques provide solutions for any health-related problems by analyzing the huge dataset. To create improved decisions about medical diagnosis and treatment choices, sufficient information to physicians, while recognizing the potential consequences and problems in each of them. Early detection can be done by the identification of disease using image processing and pursuing treatment diagnosis which may help the individual to survive. The paper focus on one of the current and responsible for the pandemic situation is COVID-19. The Early identification of COVID-19 using X-Ray images, expert’s advice as per the individual health condition helps the people’s lives to improve. The over-view of architecture with the proposed model using X-Ray image as input and predicting the current condition of a patient. It uses the clinical used data stored in Electronic Health Record (EHR) documents to solve many healthcare issues that are required, such as reducing misdiagnosis levels and predicting the outcome of procedures. The short survey focuses on various CNN architectures how they solve detection through X-ray images, individual as good combination of two architectures. The Result from the various surveys shows the combination of two CNN models provides the best result. The architectures Xception-ResNet, ResNet50V2, Mobile-Squeeze Net, VGG16 and VGG19, combined architectures give the best results according the dataset given, 99.5%, 98.49%, 99.27% and 99% respectively. Also, the research gains the ANNs knowledge, can help physicians interpret information and diagnose multiple conditions by analyzing vast quantities of data from different sources.