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 Xray diagnostics. This study focuses on deep learning models 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.