Abstract
Pneumonia remains a significant global health issue, especially in areas with limited diagnostic resources. Traditional methods for diagnosing pneumonia from chest X-rays are slow and require expert radiologists. We developed an AI-powered pneumonia detection tool using convolutional neural networks (CNNs) with VGG16 and Inception models. These models were trained on chest X-ray datasets and achieved high accuracy in classifying pneumonia. Our app, built with Flask, allows healthcare professionals to uploadX-rays and get real-time predictions with confidence scores. The system supports continuous learning through user feedback, improving its performance. This tool can potentially revolutionise pneumonia diagnosis, especially in lowresource settings.
Metadata
| Item Type: | Article |
|---|---|
| Creators: | Creators Email / ID Num. Saaidin, Shuria shuria6809@uitm.edu.my Kassim, Murizah UNSPECIFIED |
| Subjects: | R Medicine > R Medicine (General) > Neural Networks (Computer). Artificial intelligence R Medicine > RC Internal Medicine > Radiography |
| Divisions: | Universiti Teknologi MARA, Selangor > Puncak Alam Campus > Faculty of Pharmacy |
| Journal or Publication Title: | International Journal of Pharmaceuticals, Nutraceuticals and Cosmetic Science (IJPNaCS) |
| UiTM Journal Collections: | UiTM Journals > International Journal of Pharmaceuticals, Nutraceuticals and Cosmetic Science (IJPNaCS) |
| ISSN: | 2682-8146 |
| Volume: | 8 |
| Number: | Suppl1 |
| Page Range: | pp. 20-22 |
| Keywords: | Chest X-rays, Convolutional neural networks, Deep learning, Pneumonia diagnosis |
| Date: | July 2025 |
| URI: | https://ir.uitm.edu.my/id/eprint/121332 |
