Non-Invasive Technique-Based Novel Corona(COVID-19) Virus Detection Using CNN

Abstract

A novel human coronavirus 2 (SARS-CoV-2) is an extremely acute respiratory syndrome which was reported in Wuhan, China in the later half 2019. Most of its primary epidemiological aspects are not appropriately known, which has a direct effect on monitoring, practices and controls. The main objective of this work is to propose a high speed, accurate and highly sensitive CT scan approach for diagnosis of COVID19. The CT scan images display several small patches of shadows and interstitial shifts, particularly in the lung periphery. The proposed method utilizes the ResNet architecture Convolution Neural Network for training the images provided by the CT scan to diagnose the coronavirus-affected patients effectively. By comparing the testing images with the training images, the affected patient is identified accurately. The accuracy and specificity are obtained 95.09% and 81.89%, respectively, on the sample dataset based on CT images without the inclusion of another set of data such as geographical location, population density, etc. Also, the sensitivity is obtained 100% in this method. Based on the results, it is evident that the COVID-19 positive patients can be classified perfectly by using the proposed method. Keywords Coronavirus Á CT scan Á Diagnosis Á Convolution neural network Mathematics Subject Classification 94B10 Á 62M45 Significant Statement 1. This work proposes an expedite, accurate imagining approach to diagnose COVID19. 2. Small shadow patches, interstitial shifts in the lungs due to the virus are traced through CT images and are trained using ResNet architecture in CNN to obtain 95.09% accuracy. 3. When compared with RT-PCR method, this gives high accuracy, sensitivity, specificity.

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