@Article{cmc.2021.013191, AUTHOR = {Muhammad Attique Khan, Nazar Hussain, Abdul Majid, Majed Alhaisoni, Syed Ahmad Chan Bukhari, Seifedine Kadry, Yunyoung Nam, Yu-Dong Zhang}, TITLE = {Classification of Positive COVID-19 CT Scans Using Deep Learning}, JOURNAL = {Computers, Materials \& Continua}, VOLUME = {66}, YEAR = {2021}, NUMBER = {3}, PAGES = {2923--2938}, URL = {http://www.techscience.com/cmc/v66n3/41063}, ISSN = {1546-2226}, ABSTRACT = {In medical imaging, computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis. In response to the coronavirus 2019 (COVID-19) pandemic, new testing procedures, medical treatments, and vaccines are being developed rapidly. One potential diagnostic tool is a reverse-transcription polymerase chain reaction (RT-PCR). RT-PCR, typically a time-consuming process, was less sensitive to COVID-19 recognition in the disease’s early stages. Here we introduce an optimized deep learning (DL) scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography (CT) scans. In the proposed method, contrast enhancement is used to improve the quality of the original images. A pretrained DenseNet-201 DL model is then trained using transfer learning. Two fully connected layers and an average pool are used for feature extraction. The extracted deep features are then optimized with a Firefly algorithm to select the most optimal learning features. Fusing the selected features is important to improving the accuracy of the approach; however, it directly affects the computational cost of the technique. In the proposed method, a new parallel high index technique is used to fuse two optimal vectors; the outcome is then passed on to an extreme learning machine for final classification. Experiments were conducted on a collected database of patients using a 70:30 training: Testing ratio. Our results indicated an average classification accuracy of 94.76% with the proposed approach. A comparison of the outcomes to several other DL models demonstrated the effectiveness of our DL method for classifying COVID-19 based on CT scans.}, DOI = {10.32604/cmc.2021.013191} }