Optimization of convolutional neural network and visual geometry group-16 using genetic algorithms for pneumonia detection
Pneumonia is still a major global health issue, so effective diagnostic methods are needed. This research proposes a new methodology for improving convolutional neural networks (CNNs) and the Visual Geometry Group-16 (VGG16) model by incorporating genetic algorithms (GAs) to detect pneumonia. The wo...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1498403/full |
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| author | Mejda Chihaoui Mejda Chihaoui Naziha Dhibi Ahlem Ferchichi |
| author_facet | Mejda Chihaoui Mejda Chihaoui Naziha Dhibi Ahlem Ferchichi |
| author_sort | Mejda Chihaoui |
| collection | DOAJ |
| description | Pneumonia is still a major global health issue, so effective diagnostic methods are needed. This research proposes a new methodology for improving convolutional neural networks (CNNs) and the Visual Geometry Group-16 (VGG16) model by incorporating genetic algorithms (GAs) to detect pneumonia. The work uses a dataset of 5,856 frontal chest radiography images critical in training and testing machine learning algorithms. The issue relates to challenges of medical image classification, the complexity of which can be significantly addressed by properly optimizing CNN. Moreover, our proposed methodology used GAs to determine the hyperparameters for CNNs and VGG16 and fine-tune the architecture to improve the existing performance measures. The evaluation of the optimized models showed some good performances with purely convolutional neural network archetyping, averaging 97% in terms of training accuracy and 94% based on the testing process. At the same time, it has a low error rate of 0.072. Although adding this layer affected the training and testing time, it created a new impression on the test accuracy and training accuracy of the VGG16 model, with 90.90% training accuracy, 90.90%, and a loss of 0.11. Future work will involve contributing more examples so that a richer database of radiographic images is attained, optimizing the GA parameters even more, and pursuing the use of ensemble applications so that the diagnosis capability is heightened. Apart from emphasizing the contribution of GAs in improving the CNN architecture, this study also seeks to contribute to the early detection of pneumonia to minimize the complications faced by patients, especially children. |
| format | Article |
| id | doaj-art-ebcd5e3d75694b3390b5e7f8706d2f00 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-ebcd5e3d75694b3390b5e7f8706d2f002024-12-03T04:22:33ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2024-12-011110.3389/fmed.2024.14984031498403Optimization of convolutional neural network and visual geometry group-16 using genetic algorithms for pneumonia detectionMejda Chihaoui0Mejda Chihaoui1Naziha Dhibi2Ahlem Ferchichi3Computer Science Department, Applied College, University of Ha'il, Hail, Saudi ArabiaREGIM: Research Groups on Intelligent Machines, University of Sfax, National School of Engineers (ENIS), Sfax, TunisiaREGIM: Research Groups on Intelligent Machines, University of Sfax, National School of Engineers (ENIS), Sfax, TunisiaComputer Science Department, Applied College, University of Ha'il, Hail, Saudi ArabiaPneumonia is still a major global health issue, so effective diagnostic methods are needed. This research proposes a new methodology for improving convolutional neural networks (CNNs) and the Visual Geometry Group-16 (VGG16) model by incorporating genetic algorithms (GAs) to detect pneumonia. The work uses a dataset of 5,856 frontal chest radiography images critical in training and testing machine learning algorithms. The issue relates to challenges of medical image classification, the complexity of which can be significantly addressed by properly optimizing CNN. Moreover, our proposed methodology used GAs to determine the hyperparameters for CNNs and VGG16 and fine-tune the architecture to improve the existing performance measures. The evaluation of the optimized models showed some good performances with purely convolutional neural network archetyping, averaging 97% in terms of training accuracy and 94% based on the testing process. At the same time, it has a low error rate of 0.072. Although adding this layer affected the training and testing time, it created a new impression on the test accuracy and training accuracy of the VGG16 model, with 90.90% training accuracy, 90.90%, and a loss of 0.11. Future work will involve contributing more examples so that a richer database of radiographic images is attained, optimizing the GA parameters even more, and pursuing the use of ensemble applications so that the diagnosis capability is heightened. Apart from emphasizing the contribution of GAs in improving the CNN architecture, this study also seeks to contribute to the early detection of pneumonia to minimize the complications faced by patients, especially children.https://www.frontiersin.org/articles/10.3389/fmed.2024.1498403/fullpneumoniadeep learningconvolutional neural networkgenetic algorithmvisual geometry group-16 |
| spellingShingle | Mejda Chihaoui Mejda Chihaoui Naziha Dhibi Ahlem Ferchichi Optimization of convolutional neural network and visual geometry group-16 using genetic algorithms for pneumonia detection Frontiers in Medicine pneumonia deep learning convolutional neural network genetic algorithm visual geometry group-16 |
| title | Optimization of convolutional neural network and visual geometry group-16 using genetic algorithms for pneumonia detection |
| title_full | Optimization of convolutional neural network and visual geometry group-16 using genetic algorithms for pneumonia detection |
| title_fullStr | Optimization of convolutional neural network and visual geometry group-16 using genetic algorithms for pneumonia detection |
| title_full_unstemmed | Optimization of convolutional neural network and visual geometry group-16 using genetic algorithms for pneumonia detection |
| title_short | Optimization of convolutional neural network and visual geometry group-16 using genetic algorithms for pneumonia detection |
| title_sort | optimization of convolutional neural network and visual geometry group 16 using genetic algorithms for pneumonia detection |
| topic | pneumonia deep learning convolutional neural network genetic algorithm visual geometry group-16 |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1498403/full |
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