Neural Networks Based Smart e-Health Application for the Prediction of Tuberculosis Using Serverless Computing
Published in IEEE Journal of Biomedical and Health Informatics, 2024
We propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures. The best-performing model, VGG-19, is deployed using server and serverless-based environments with performance measured using JMeter.
Keywords: Biomedical imaging, X-ray imaging, Tuberculosis, Image recognition, e-Health, Machine Learning, Serverless Computing, Healthcare, IoT
Recommended citation: SS Murugesan, S Velu, M Golec, H Wu, SS Gill. "Neural Networks Based Smart e-Health Application for the Prediction of Tuberculosis Using Serverless Computing," IEEE Journal of Biomedical and Health Informatics 28(9), 5765-5776 (2024). DOI: 10.1109/JBHI.2024.3367736
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