Classification of Positive COVID-19 CT Scans Using Deep Learning
- Authors
- Khan, Muhammad Attique; Hussain, Nazar; Majid, Abdul; Alhaisoni, Majed; Bukhari, Syed Ahmad Chan; Kadry, Seifedine; Nam, Yunyoung; Zhang, Yu-Dong
- Issue Date
- 2021
- Publisher
- Tech Science Press
- Keywords
- Coronavirus; contrast enhancement; deep learning; features optimization; fusion; classification
- Citation
- Computers, Materials and Continua, v.66, no.3, pp 2923 - 2938
- Pages
- 16
- Journal Title
- Computers, Materials and Continua
- Volume
- 66
- Number
- 3
- Start Page
- 2923
- End Page
- 2938
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2210
- DOI
- 10.32604/cmc.2021.013191
- ISSN
- 1546-2218
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.
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