Deep Learning CNN for Pneumonia Detection: Advancing Digital Health in Society 5.0
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915797817884672 |
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| author | Almohab, Hadi |
| author_facet | Almohab, Hadi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13270 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Learning CNN for Pneumonia Detection: Advancing Digital Health in Society 5.0 Almohab, Hadi Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Deep Learning CNN for Pneumonia Detection: Advancing Digital Health in Society 5.0 |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.13270 |