Deep Learning CNN for Pneumonia Detection: Advancing Digital Health in Society 5.0

TL;DR

This paper presents a CNN-based deep learning model for automatic pneumonia detection from chest X-ray images, achieving high accuracy and supporting healthcare advancements in Society 5.0.

Contribution

The study develops an optimized CNN model with preprocessing techniques that significantly improves pneumonia detection accuracy from X-ray images.

Findings

Achieved 91.67% accuracy in pneumonia detection

ROC-AUC of 0.96 indicating strong discriminative ability

Model demonstrates potential as a reliable diagnostic aid

Abstract

Pneumonia is a serious global health problem, contributing to high morbidity and mortality, especially in areas with limited diagnostic tools and healthcare resources. This study develops a Convolutional Neural Network (CNN) based on deep learning to automatically detect pneumonia from chest X-ray images. The method involves training the model on labeled datasets with preprocessing techniques such as normalization, data augmentation, and image quality enhancement to improve robustness and generalization. Testing results show that the optimized model achieves 91.67% accuracy, ROC-AUC of 0.96, and PR-AUC of 0.95, demonstrating strong performance in distinguishing pneumonia from normal images. In conclusion, this CNN model has significant potential as a fast, consistent, and reliable diagnostic aid, supporting Society 5.0 by integrating artificial intelligence to improve healthcare services and public well-being.