Corona-Virus Detection Using Web Management Platform For CT Scans

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Abstract

Disease spreads from viruses, like COVID-19, stem from agents like SARS-CoV-2. Common symptoms associated with this virus include fever, cough, indigestion, muscle pain, and fatigue. Across many nations, the RT-PCR test stands out as the predominant molecular test employed for tracking virus transmission. There is, however, a long processing time, and the ingredients are in short supply. This work proposes to use chest CT scan images as input to identify patients with COVID-19 by utilizing a deep neural network architecture. The stages like feature extraction, where the features of the picture are extracted using a pre-trained model called VGG16. The second phase in which a multilayer neural network classifies the image based on its COVID classification and NO COVID classification. Implementing a Web platform that makes our architecture easy for interested people to understand, access, and use. Using Python libraries for neural network design, the deep learning algorithm was implemented.

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Keywords

Artificial Intelligence (AI); Convolution Neural Networks (CNN's); COVID-19; Deep Learning Algorithm; Neural Network Design; Under-Fitting and Over-Fitting; Xray Chest Images