Covid-19 Prediction from Chest X-Ray and CT scan Using Deep Learning Methods

The novel coronavirus disease of humans, COVID-19, is now thought to be the deadliest sickness it has ever produced. A limited number of COVID-19 test kits are accessible in hospitals because of a shortage of radiologists, and this is also accompanied by a shortage of equipment due to the daily increase in cases as a result of an increase in the number of COVID-19-infected individuals. To Overcome this Several convolutional neural networks (CNN)-based techniques for computer-aided COVID-19 detection based on lung computed tomography (CT) scans and Chest X-ray Images (C-X-rays) have been developed as of late. In this proposed model we used a hybrid dataset of Chest X-ray and CT scans in a single dataset to train the model and predict the COVID-19 cases with both the Chest X-ray and CT scan. However, the presentations of COVID-19 in CT scans and Chest X-Rays are classified into two classes Covid 19 and Normal. Chest X-rays and CT scans provide a different method for detecting Coronavirus early in the disease phase. Using VGG16 and CNN deep learning algorithms, the model identified characteristics from Chest X-ray images and CT scans and categorized them into two groups: Normal and COVID-19. The model had trained with 1600 pictures of two folders test and validation with 2 classes of each folder to test their realism in practical settings. The traditional CNN model has less accuracy in detecting COVID-19 cases, for the betterment of prediction it needs the improved CNN model. The proposed model has the highest accuracy of 94% when compared to the traditional CNN model.