Local search enhanced optimal Inception-ResNet-v2 for classification of long-term lung diseases in post-COVID-19 patients
The Coronavirus disease (COVID-19) has emerged as a global epidemic, posing a significant threat to countries worldwide. COVID-19 is closely associated with pneumonia, leading to the unfortunate loss of many lives due to pulmonary conditions. Differentiating between pneumonia and COVID-19 based on c...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-04-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2023.2295142 |
| Summary: | The Coronavirus disease (COVID-19) has emerged as a global epidemic, posing a significant threat to countries worldwide. COVID-19 is closely associated with pneumonia, leading to the unfortunate loss of many lives due to pulmonary conditions. Differentiating between pneumonia and COVID-19 based on chest X-ray images has become a challenging task. This paper proposes a Local Search Enhanced AHO-based Inception-ResNet-v2 Model to develop a robust and accurate classification model for identifying and categorizing chronic lung diseases in patients who have recovered from COVID-19. The proposed model utilizes the Inception-ResNet-v2 architecture to extract features from CT scan images, which are then used to classify the lung diseases present in the patients. A curated dataset of CT scan images from post-COVID-19 patients with known lung disease classes is used to train the model. Experimental results demonstrate that the proposed method achieves an accuracy of 98.97%, precision of 98.95%, sensitivity of 98.91%, F-score of 98.86%, and specificity of 98.89%. These performance metrics are comparable to those achieved by methods based on manually delineated contaminated areas. |
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| ISSN: | 0005-1144 1848-3380 |