Enhancing Pneumonia Disease Classification using Genetic Algorithm-Tuned DCGANs and VGG-16 Integration
Diagnostic complications arise from pneumonia, characterized by lung inflammation caused by alveolar fluid accumulation, particularly in regions with limited radiologists. To tackle this issue, a new method utilizes the VGG16 methodology for categorization, bolstered by genetic algorithms. In additi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Poltekkes Kemenkes Surabaya
2023-12-01
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| Series: | Journal of Electronics, Electromedical Engineering, and Medical Informatics |
| Subjects: | |
| Online Access: | https://jeeemi.org/index.php/jeeemi/article/view/349 |
| _version_ | 1797337485627359232 |
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| author | Kania Ardhani Putri Wikky Fawwaz Al Maki |
| author_facet | Kania Ardhani Putri Wikky Fawwaz Al Maki |
| author_sort | Kania Ardhani Putri |
| collection | DOAJ |
| description | Diagnostic complications arise from pneumonia, characterized by lung inflammation caused by alveolar fluid accumulation, particularly in regions with limited radiologists. To tackle this issue, a new method utilizes the VGG16 methodology for categorization, bolstered by genetic algorithms. In addition, Deep Convolutional Generative Adversarial Networks (DCGANs) improve the dataset by adding fake X-rays of pneumonia. Genetic algorithms are used to optimize hyperparameters in classification tasks. In contrast, DCGANs are employed to increase data augmentation techniques, boosting models' accuracy in identifying and categorizing pneumonia cases. The study partitioned a dataset into training, testing, and validation sets for pneumonia X-ray pictures. The training of GANs entails utilizing both generators and discriminators to produce increasingly realistic pictures gradually. The genetic algorithm enhances the hyperparameter tuning process, resulting in a substantial increase in accuracy. Initially, VGG16 achieved a success rate of 89.50% and a fitness score of 87.50%. Post-optimization and DCGAN augmentation, accuracy climbed to 95.50%, and F1-Score improved to 94.75%. This study combines genetic algorithms and DCGANs to create a model that can produce genuine pneumonia X-ray pictures and enhance categorization accuracy. |
| first_indexed | 2024-03-08T09:11:17Z |
| format | Article |
| id | doaj.art-67c9d89180a6484fbedda7eb85012192 |
| institution | Directory Open Access Journal |
| issn | 2656-8632 |
| language | English |
| last_indexed | 2024-03-08T09:11:17Z |
| publishDate | 2023-12-01 |
| publisher | Poltekkes Kemenkes Surabaya |
| record_format | Article |
| series | Journal of Electronics, Electromedical Engineering, and Medical Informatics |
| spelling | doaj.art-67c9d89180a6484fbedda7eb850121922024-01-31T23:55:01ZengPoltekkes Kemenkes SurabayaJournal of Electronics, Electromedical Engineering, and Medical Informatics2656-86322023-12-0161112210.35882/jeeemi.v6i1.349349Enhancing Pneumonia Disease Classification using Genetic Algorithm-Tuned DCGANs and VGG-16 IntegrationKania Ardhani Putri0Wikky Fawwaz Al Maki1School of Computing, Telkom University, Bandung, IndonesiaSchool of Computing, Telkom University, Bandung, IndonesiaDiagnostic complications arise from pneumonia, characterized by lung inflammation caused by alveolar fluid accumulation, particularly in regions with limited radiologists. To tackle this issue, a new method utilizes the VGG16 methodology for categorization, bolstered by genetic algorithms. In addition, Deep Convolutional Generative Adversarial Networks (DCGANs) improve the dataset by adding fake X-rays of pneumonia. Genetic algorithms are used to optimize hyperparameters in classification tasks. In contrast, DCGANs are employed to increase data augmentation techniques, boosting models' accuracy in identifying and categorizing pneumonia cases. The study partitioned a dataset into training, testing, and validation sets for pneumonia X-ray pictures. The training of GANs entails utilizing both generators and discriminators to produce increasingly realistic pictures gradually. The genetic algorithm enhances the hyperparameter tuning process, resulting in a substantial increase in accuracy. Initially, VGG16 achieved a success rate of 89.50% and a fitness score of 87.50%. Post-optimization and DCGAN augmentation, accuracy climbed to 95.50%, and F1-Score improved to 94.75%. This study combines genetic algorithms and DCGANs to create a model that can produce genuine pneumonia X-ray pictures and enhance categorization accuracy.https://jeeemi.org/index.php/jeeemi/article/view/349pneumoniadeep learningvgg16genetic algorithmdeep convolutional generative adversarial network |
| spellingShingle | Kania Ardhani Putri Wikky Fawwaz Al Maki Enhancing Pneumonia Disease Classification using Genetic Algorithm-Tuned DCGANs and VGG-16 Integration Journal of Electronics, Electromedical Engineering, and Medical Informatics pneumonia deep learning vgg16 genetic algorithm deep convolutional generative adversarial network |
| title | Enhancing Pneumonia Disease Classification using Genetic Algorithm-Tuned DCGANs and VGG-16 Integration |
| title_full | Enhancing Pneumonia Disease Classification using Genetic Algorithm-Tuned DCGANs and VGG-16 Integration |
| title_fullStr | Enhancing Pneumonia Disease Classification using Genetic Algorithm-Tuned DCGANs and VGG-16 Integration |
| title_full_unstemmed | Enhancing Pneumonia Disease Classification using Genetic Algorithm-Tuned DCGANs and VGG-16 Integration |
| title_short | Enhancing Pneumonia Disease Classification using Genetic Algorithm-Tuned DCGANs and VGG-16 Integration |
| title_sort | enhancing pneumonia disease classification using genetic algorithm tuned dcgans and vgg 16 integration |
| topic | pneumonia deep learning vgg16 genetic algorithm deep convolutional generative adversarial network |
| url | https://jeeemi.org/index.php/jeeemi/article/view/349 |
| work_keys_str_mv | AT kaniaardhaniputri enhancingpneumoniadiseaseclassificationusinggeneticalgorithmtuneddcgansandvgg16integration AT wikkyfawwazalmaki enhancingpneumoniadiseaseclassificationusinggeneticalgorithmtuneddcgansandvgg16integration |