EECS MSc student, Subramaniam Subramanian Murugesan, has published his MSc dissertation in IEEE Journal of Biomedical and Health Informatics (Q1 Journal with IF: 7.7).
Subramaniam Subramanian Murugesan’s published dissertation investigated into Neural Networks based Smart e-Health Application for the prediction of Tuberculosis using Serverless computing.
Subramaniam said:
“I’m thrilled to share that my research article, titled ‘Neural Networks based Smart e-Health Applications for the Prediction of Tuberculosis Using Serverless Computing,’ has been published in the IEEE Journal of Biomedical and Health Informatics. This significant milestone in my academic journey has fuelled my passion for machine learning, artificial intelligence, and the broader computing domain. The acknowledgment of my work by such a renowned journal has been an incredibly rewarding experience. It signifies my first major contribution to the academic community, setting a foundation for my future endeavours in both research and industry applications.
I owe a great deal of this success to my esteemed supervisor, Dr. Sukhpal Singh Gill, whose expert guidance and unwavering support have been invaluable throughout this process. I’d like to extend my heartfelt gratitude to my co-authors for their invaluable contributions to our paper.”
Publication Abstract:
The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove that this technique holds promise for enhancing the capabilities of IoT and e-health systems in the future for predicting the manifestation of TB in the lungs. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures based on performance metrics such as test binary accuracy, test loss, intersection over union, precision, recall, and F1 score. The findings hint at the potential of integrating these advanced Machine Learning (ML) models within IoT and e-health frameworks, thereby paving the way for more comprehensive and data-driven approaches to enable smart healthcare. The best-performing model, VGG-19, is selected for different deployment strategies using server and serverless-based environments. We used JMeter to measure the performance of the deployed model, including the average response rate, throughput, and error rate. This study provides valuable insights into the selection and deployment of ML models in healthcare, highlighting the advantages and challenges of different deployment options. Furthermore, it also allows future studies to integrate such models into IoT and e-health systems, which could enhance healthcare outcomes through more informed and timely treatments.
Software availability
The researchers have released code as open-source software. The implementation code with experiment scripts and results can be found at the GitHub repository: https://github.com/Subramaniam-dot/e-HealthcareFaaS
For further information, watch this video on YouTube: https://youtu.be/0Kom5JOVsiY
Publication Details:
Subramaniam Subramanian Murugesan, Sasidharan Velu, Muhammed Golec, Huaming Wu, Sukhpal Singh Gill. Neural Networks based Smart e-Health Application for the Prediction of Tuberculosis using Serverless Computing. IEEE Journal of Biomedical and Health Informatics, 2023. https://doi.org/10.1109/JBHI.2024.3367736
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