Classification of Pneumonia from Chest X-ray images using Support Vector Machine and Convolutional Neural Network DOI Open Access
M. Fariz Fadillah Mardianto,

Alfredi Yoani,

Steven Soewignjo

et al.

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(6)

Published: Jan. 1, 2024

Pneumonia presents a global health challenge, especially in distinguishing bacterial and viral types via chest X-ray diagnostics. This study focuses on deep learning models Convolutional Neural Networks (CNN) Support Vector Machines (SVM) for pneumonia classification. Our findings highlight CNN's superior performance. It achieves 91% accuracy overall, outperforming SVM's 79% differentiating normal lungs pneumonia-affected lungs. Specifically, CNN excels between with 92% accuracy, compared to 88%. These results underscore models' potential enhance diagnostic precision, improve treatment efficacy reduce pneumonia-related mortality. In the context of Society 5.0, which integrates technology societal well-being, healthcare emerges as transformative. Enabling early accurate detection, this research aligns United Nations Sustainable Development Goals (SDGs). supports Goal 3 (Good Health Well-being) by advancing outcomes 9 (Industry, Innovation, Infrastructure) through innovative medical Therefore, emphasizes learning's pivotal role revolutionizing diagnosis, offering efficient solutions aligned current challenges.

Language: Английский

Classification of Pneumonia from Chest X-ray images using Support Vector Machine and Convolutional Neural Network DOI Open Access
M. Fariz Fadillah Mardianto,

Alfredi Yoani,

Steven Soewignjo

et al.

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(6)

Published: Jan. 1, 2024

Pneumonia presents a global health challenge, especially in distinguishing bacterial and viral types via chest X-ray diagnostics. This study focuses on deep learning models Convolutional Neural Networks (CNN) Support Vector Machines (SVM) for pneumonia classification. Our findings highlight CNN's superior performance. It achieves 91% accuracy overall, outperforming SVM's 79% differentiating normal lungs pneumonia-affected lungs. Specifically, CNN excels between with 92% accuracy, compared to 88%. These results underscore models' potential enhance diagnostic precision, improve treatment efficacy reduce pneumonia-related mortality. In the context of Society 5.0, which integrates technology societal well-being, healthcare emerges as transformative. Enabling early accurate detection, this research aligns United Nations Sustainable Development Goals (SDGs). supports Goal 3 (Good Health Well-being) by advancing outcomes 9 (Industry, Innovation, Infrastructure) through innovative medical Therefore, emphasizes learning's pivotal role revolutionizing diagnosis, offering efficient solutions aligned current challenges.

Language: Английский

Citations

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