Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2023.2295142
Local search enhanced optimal Inception-ResNet-v2 for classification of long-term lung diseases in post-COVID-19 patients
Anusha Sanampudi
; Department of Artificial Intelligence and Data Science, R.M.K. Engineering College, Kavaraipettai, India
*
S. Srinivasan
; Department of Computer Science and Engineering, R.M.D. Engineering College, Kavaraipettai, India
* Dopisni autor.
Sažetak
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%, Fscore of 98.86%, and specificity of 98.89%. These performance metrics are comparable to those
achieved by methods based on manually delineated contaminated areas.
Ključne riječi
COVID-19; lung disease; deep learning; classification; optimization
Hrčak ID:
323039
URI
Datum izdavanja:
15.1.2024.
Posjeta: 0 *