Experimental Analysis Using Hybrid Convolutional Neural Networks, Gradient Boosting Classifier, and Differential Algorithm for Detection of COVID-19 from X-Ray Images
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 2 |
| Year of Publication : 2024 |
| Authors : S. Akila, S. Prasanna |
10.14445/23488549/IJECE-V11I2P102
How to Cite?
S. Akila, S. Prasanna, "Experimental Analysis Using Hybrid Convolutional Neural Networks, Gradient Boosting Classifier, and Differential Algorithm for Detection of COVID-19 from X-Ray Images," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 2, pp. 9-23, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I2P102
Abstract:
A significant number of individuals have lost their lives due to the new COVID-19 virus. The coronavirus has ruined many people’s lives, and the healthcare system is struggling a lot because of it. Since the virus can harm the lungs severely, it’s essential to find it early. To detect COVID-19 from X-ray images, this study presents a novel hybrid approach that combines convolutional neural networks, gradient-boosting classifiers, and differential algorithms. This strategy offers a synergistic fusion of deep learning, ensemble learning, and optimization strategies. In the context of COVID-19 detection by X-ray imaging, the adaptive integration of these disparate methodologies constitutes a groundbreaking attempt to address the shortcomings of current methods and significantly improve diagnostic accuracy. This study recommends using a computer program called CNN to help identify COVID-19 in chest X-ray images. For this study, scientists used a collection of 13,000 chest X-ray pictures. With CLAHE’s help, researchers improved the original dataset. The research used advanced computer programs to find essential details in the pictures and then used a method to focus on the most valuable parts. To prevent overfitting, the model locks in the weights of the dense layers trained in previous rounds. This enables it to fit the new thick layer and optimize the convolutional layers while retaining the previously learned data. The final layer of the CNN Model was replaced with the Gradient Boosting Machines classifier for classification. The results showed that the suggested approach was 98% specific, 97% sensitive, and 98% accurate. According to study data, the suggested approach performed better than previous COVID-19 detection investigations based on X-ray imaging.
Keywords:
COVID-19, Contrast Limited Adaptive Histogram Equalization (CLAHE), Neural Networks, Gradient Boosting Machines, Differential Feature Selection algorithm.
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