A Hybrid Deep Learning Approach for COVID-19 Diagnosis via CT and X-ray Medical Images †
Abstract
:1. Introduction
2. Related Work
3. Methods and Materials
3.1. Preprocessing
3.2. Feature Extraction
3.3. Classification
4. Experimental Analysis
4.1. Dataset
4.2. Implementation Environment
4.3. Evaluation Metrics
5. Results and Discussion
5.1. Single Straightforward Scenario
5.1.1. COVID-19 Classification Based on Chest X-ray Images
5.1.2. COVID-19 Classification Based on Chest CT Images
5.2. Hybrid Scenario: COVID-19 Classification Using Chest and X-ray Images
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Type of Images | COVID | Normal |
---|---|---|
CT | 1323 | 1290 |
X-ray | 3923 | 3960 |
Model | Az | Sp | Re | F-M |
---|---|---|---|---|
ResNet50 | 98.47 | 99.0 | 100 | 99.0 |
EfficientNetB0 | 99.36 | 98.0 | 99.0 | 99.0 |
Model | Az | Sp | Re | F-M |
---|---|---|---|---|
ResNet50 | 98.85 | 99.0 | 98.0 | 99.0 |
EfficientNetB0 | 99.23 | 99.0 | 99.0 | 99.0 |
Model | Az | Sp | Re | F-M |
---|---|---|---|---|
ResNet50 | 98.01 | 99.0 | 99.0 | 99.0 |
EfficientNetB0 | 99.58 | 99.0 | 99.0 | 99.0 |
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Chola, C.; Mallikarjuna, P.; Muaad, A.Y.; Bibal Benifa, J.V.; Hanumanthappa, J.; Al-antari, M.A. A Hybrid Deep Learning Approach for COVID-19 Diagnosis via CT and X-ray Medical Images. Comput. Sci. Math. Forum 2022, 2, 13. https://doi.org/10.3390/IOCA2021-10909
Chola C, Mallikarjuna P, Muaad AY, Bibal Benifa JV, Hanumanthappa J, Al-antari MA. A Hybrid Deep Learning Approach for COVID-19 Diagnosis via CT and X-ray Medical Images. Computer Sciences & Mathematics Forum. 2022; 2(1):13. https://doi.org/10.3390/IOCA2021-10909
Chicago/Turabian StyleChola, Channabasava, Pramodha Mallikarjuna, Abdullah Y. Muaad, J. V. Bibal Benifa, Jayappa Hanumanthappa, and Mugahed A. Al-antari. 2022. "A Hybrid Deep Learning Approach for COVID-19 Diagnosis via CT and X-ray Medical Images" Computer Sciences & Mathematics Forum 2, no. 1: 13. https://doi.org/10.3390/IOCA2021-10909