Classification of Pneumonia from Chest X-ray images using Support Vector Machine and Convolutional Neural Network

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

@article{Mardianto2024,
  title     = {Classification of Pneumonia from Chest X-ray images using Support Vector Machine and Convolutional Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {M. Fariz Fadillah Mardianto and Alfredi Yoani and Steven Soewignjo and I Kadek Pasek Kusuma Adi Putra and Deshinta Arrova Dewi},
  doi       = {10.14569/IJACSA.2024.01506104},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506104}
}