Advancing IoT Security in Medical Imaging with Enhanced CNN Architectures
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 11 |
| Year of Publication : 2024 |
| Authors : Naga Venkata Rama Krishna Guduri, Beera John Jaidhan |
10.14445/23488379/IJEEE-V11I11P105
How to Cite?
Naga Venkata Rama Krishna Guduri, Beera John Jaidhan, "Advancing IoT Security in Medical Imaging with Enhanced CNN Architectures," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 11, pp. 44-52, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I11P105
Abstract:
This paper delves into implementing a cutting-edge Convolution Neural Network (CNN) architecture to identify abnormalities in medical images seamlessly integrated within an IoT-enabled healthcare system. The primary objective is to enhance data security and improve diagnostic accuracy by leveraging deep learning techniques. The model presented in this study incorporates advanced CNN enhancements, such as attention mechanisms and transfer learning, to maximize performance and guarantee strong security in transmitting and processing medical data. This comprehensive study delves into the methodology, implementation, and evaluation of a groundbreaking approach. Our aim is to provide a detailed framework for harnessing the power of IoT in the field of medical imaging, all while tackling the critical security challenges that arise.
Keywords:
Artificial Intelligence, Deep Learning, IoT, IoMT, CNN.
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