A novel deep learning based method for COVID-19 detection from CT image.
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| Title: | A novel deep learning based method for COVID-19 detection from CT image. |
|---|---|
| Authors: | JavadiMoghaddam S; Department of Computer Engineering, Bozorgmehr University of Qaenat, Qaen, Iran., Gholamalinejad H; Department of Computer Science, Bozorgmehr University of Qaenat, Qaen. Iran. |
| Source: | Biomedical signal processing and control [Biomed Signal Process Control] 2021 Sep; Vol. 70, pp. 102987. Date of Electronic Publication: 2021 Jul 21. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Elsevier Country of Publication: England NLM ID: 101317299 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1746-8094 (Print) Linking ISSN: 17468094 NLM ISO Abbreviation: Biomed Signal Process Control Subsets: PubMed not MEDLINE |
| Imprint Name(s): | Original Publication: Oxford, UK : Elsevier, c2006- |
| Abstract: | The novel Coronavirus named COVID-19 that World Health Organization (WHO) announced as a pandemic rapidly spread worldwide. Fast diagnosis of the virus infection is critical to prevent further spread of the virus, help identify the infected population, and cure the patients. Due to the increasing rate of infection and the limitations of the diagnosis kit, auxiliary detection tools are needed. Recent studies show that a deep learning model that comes up with the salient information of CT images can aid in the COVID-19 diagnosis. This study proposes a novel deep learning structure that the pooling layer of this model is a combination of pooling and the Squeeze Excitation Block (SE-block) layer. The proposed model uses Batch Normalization and Mish Function to optimize convergence time and performance of COVID-19 diagnosis. A dataset of two public hospitals was used to evaluate the proposed model. Moreover, it was compared to some different popular deep neural networks (DNN). The results expressed an accuracy of 99.03 with a recognition time of test mode of 0.069 ms in graphics processing unit (GPU). Furthermore, the best network results in classification metrics parameters and real-time applications belong to the proposed model. (© 2021 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
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| Contributed Indexing: | Keywords: Batch normalization; COVID-19 detection method; Deep learning model; Disease diagnosis; Mish function |
| Entry Date(s): | Date Created: 20210804 Latest Revision: 20240908 |
| Update Code: | 20250114 |
| PubMed Central ID: | PMC8318781 |
| DOI: | 10.1016/j.bspc.2021.102987 |
| PMID: | 34345248 |
| Database: | MEDLINE |
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