A Modern Replica for COVID-19 Pestilential Disease Identification

2022 3rd International Conference on Intelligent Engineering and Management (ICIEM)(2022)

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Detection of COVID-19 disease and its unmasking, demands a certain level of proficiency. The Work exhibited in the paper proposes a novel Deep Learning based approach to recognize COVID-19 contagious infection using CT scans and X- Rays of lungs in Humans. So that labour and risk intensive task for radiotherapists of taking samples from the patients can be minimized and risk of community spread can be avoided. Our model takes into the CT scan chest images of the patient having a certainty of infection and returns the most significant disease category related to that patient. In our study, we demonstrated a Deep Learning framework model that follows the methodology of up-skilled feature extraction techniques along with Logistic Regression [LR] and other usable classifiers. This is used on images to detect and report the presence of infection that is being prevailed in an organ with a considerably pinpoint accuracy of 97.8%. Also after trying the model on spatial information real- time dataset of our Family members, who were infected by the disease, this model was able to detect 8 out of 10 images correctly.
Logistic Regression[LR],Inception V3,VGG- 16,VGG-19,DeepLoc,SqueezeNet,Painters,Stochastic Gradient Decent [SGD],Support Vector machine[SVM],Fine tuning and feature extraction
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