Title: Enhancing pneumonia diagnosis through pre-processing approaches and advanced AI models: a comparative study and deployment on web and mobile platforms

Authors: Ngoc-Phu Huynh; Thi-Lua Ngo; Thi-Thu-Hien Pham; Tan-Nhu Nguyen; Nhat-Minh Nguyen; Ngoc-Bich Le

Addresses: School of Biomedical Engineering, International University, Ho Chi Minh City, 700000, Vietnam ' School of Biomedical Engineering, International University, Ho Chi Minh City, 700000, Vietnam; Vietnam National University HCMC, Ho Chi Minh City, 700000, Vietnam ' School of Biomedical Engineering, International University, Ho Chi Minh City, 700000, Vietnam; Vietnam National University HCMC, Ho Chi Minh City, 700000, Vietnam ' School of Biomedical Engineering, International University, Ho Chi Minh City, 700000, Vietnam; Vietnam National University HCMC, Ho Chi Minh City, 700000, Vietnam ' School of Biomedical Engineering, International University, Ho Chi Minh City, 700000, Vietnam ' School of Biomedical Engineering, International University, Ho Chi Minh City, 700000, Vietnam; Vietnam National University HCMC, Ho Chi Minh City, 700000, Vietnam

Abstract: Pneumonia, often diagnosed through X-ray imaging, is susceptible to diagnostic errors due to subjective interpretation. Machine learning offers a solution by aiding physicians in reducing these errors and saving time. This study employed various convolutional neural network (CNN) models - Basic CNN, VGG16, and EfficientNet - alongside the vision transformer (ViT) for image processing. Pre-processing techniques were applied to augment the dataset and enhance model performance, yielding satisfactory average area under the curve (AUC) scores on the test set. The most accurate and stable models were deployed on both mobile and web applications for physician accessibility. ViT demonstrated exceptional performance, achieving 95% accuracy on training and validation sets, and 97.5% on the test set. VGG16, despite its age, also performed well with 90% accuracy on training and validation sets, and 91.2% on the test set. While VGG16 performed reliably on both platforms, ViT faced challenges in web deployment due to platform disparities.

Keywords: pneumonia; lungs diseases; vision transformer; ViT; VGG16; CNN; EfficientNet; deployment.

DOI: 10.1504/IJBET.2025.146422

International Journal of Biomedical Engineering and Technology, 2025 Vol.48 No.1, pp.27 - 54

Received: 31 Jul 2024
Accepted: 18 Oct 2024
Published online: 28 May 2025 *

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