Evaluating Median Accuracy of ResNet50 and VGG16 Models in COVID-19 Detection (International Journal of Innovative Research in Computer Science & Technology, 2023)
The increasing number of Covid-19 cases  and the lack of reliable, quick-to-use testing tools herald a  new era in X-ray analysis employing deep learning  methods. The Covid-19 virus's emergence poses a threat to  human existence. Therefore, it will take time to develop a  quick and accurate method of identifying the Covid-19  virus in patients. The reference method is the conventional  RT-PCR technique. The goal of this research is to create an  automated system that uses CNN models such as Resnet50,  VGG16, and Grad-CAM to analyze X-ray images in order  to provide a reliable and effective method of diagnosing  Covid-19 infection. The created models use image  processing techniques to pre-process the X-ray picture.  Afterwards, deep learning is used to classify the images  after they have been segmented and transformed. The CNN  model that is being used provides strong classification  accuracy and shows the location in the lung where the  disease is attacked, even for a normal person, we can  anticipate the likelihood of where the COVID may affect  them. Our model utilizes a convolution neural network that  is trained on the standard COVID-19 Radiography Dataset.
Keywords: CNNResnet50; VGG16; Grad-CAM; Covid