University of Bahrain
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Covid - 19 Patient's CT Images classification: StackAlexNet-19 A Deep Learning Approach

Show simple item record Sahu, Barnali Saurabh, Siddharth Swarnakar, Tripti 2022-12-30T18:42:45Z 2022-12-30T18:42:45Z 2022-12-30
dc.identifier.issn 2210-142X
dc.description.abstract At present, the whole world is infected by COVID-19. It targets affecting the respiratory system and worsens for people with other health complications like diabetes, cardiovascular diseases, cancer, lung disorder, and so on. The availability of test kits is not adequate and symptoms of COVID-19 similar to pneumonia are deadly, which claims millions of people. The COVID-19 test kits are time-consuming and even reduce the detection rate. Therefore, in the current study, an automatic CT image classification technique for COVID-19 and Non-COVID patient identification is proposed. In this paper, a StackedAlexNet-19 convolution network for automatic classification of lung CT images is proposed. The proposed StackedAlexNet-19 model consists of different pre-trained methods like ResNet 101, Xception, NASNet, MobileNet, and InceptionV3. Based on the pre-trained model, input CT images are processed and integrated for the detection of abnormalities in COVID-19 CT images of patients. The StackAleNet-19 model is evaluated and comparatively examined with the existing techniques. The dataset for processing consists of 1359 CT images composed of COVID, non-COVID, and other infections. The validation range is set as 50 for each case with a total value of 150 and the network is trained with CT images of 1069 for classification. The analysis of results expressed that StackAlexNet-19 exhibits higher accuracy, sensitivity, and specificity value of 93.67%, 0.93, and 0.97 respectively. The proposed StackAlexNet classification technique achieves an accuracy of 93.67%. The developed model provides improved accuracy than the existing techniques. The StackAlexNet-19 facilitates the intervention of COVID-19 without any human intervention. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject COVID-19, StackAlexNet-19, Deep Learning, Stacking, Image Classification en_US
dc.title Covid - 19 Patient's CT Images classification: StackAlexNet-19 A Deep Learning Approach en_US
dc.type Article en_US
dc.volume 12 en_US
dc.issue 1 en_US
dc.pagestart 1453 en_US
dc.pageend 1464 en_US
dc.contributor.authoraffiliation Department of Computer Science and EngineeringSiksha’O’Anusandhan University, Bhubaneswar, Odisha,India en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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