A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are needed.The enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting COVID-19.The most common symptoms of COVID-19 are fever,dry cough and sore throat.These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier.Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death rate.Here,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and classification.This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models.At last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of class.With the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is estimated.The experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.