Hasan, Ali M. and AL-Jawad, Mohammed M. and Jalab, Hamid A. and Shaiba, Hadil and Ibrahim, Rabha W. and AL-Shamasneh, Ala'a R. (2020) Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features. Entropy, 22 (5). ISSN 1099-4300, DOI https://doi.org/10.3390/E22050517.
Full text not available from this repository.Abstract
Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists' efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.
| Item Type: | Article |
|---|---|
| Funders: | Deanship of Scientific Research at Princess Nourah bint Abdulrahman University, Princess Nourah Bint Abdulrahman University, National Cancer Institute, University of Chicago |
| Uncontrolled Keywords: | Deep learning; CT scans of lungs; Fractional calculus; Q-deformed entropy; Features extraction; LSTM network |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) > Medical technology |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 05 Nov 2024 07:31 |
| Last Modified: | 05 Nov 2024 07:31 |
| URI: | http://eprints.um.edu.my/id/eprint/36720 |
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