Detection of Early-Stage Breast Cancer using Electromagnetic Sensor with Aid of the Convolution Neural Network

H J Prashanthi; T Prathibha; D Thippesha

doi:10.17485/IJST/v17i1.1922

Article

Detection of Early-Stage Breast Cancer using Electromagnetic Sensor with Aid of the Convolution Neural Network

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i1.1922

Year: 2024, Volume: 17, Issue: 1, Pages: 65-69

Original Article

Detection of Early-Stage Breast Cancer using Electromagnetic Sensor with Aid of the Convolution Neural Network

H J Prashanthi¹, T Prathibha², D Thippesha^3*

¹Assistant Professor, ECE Department, Government Engineering College, Devagiri, Haveri, Karnataka, India
²Assistant Professor, CSE Department, Government Engineering College, Ramanagara, Karnataka, India
³Member, Institute of Electrical and Electronics Engineers, Bangalore, Karnataka, India

*Corresponding Author
Email: [email protected]

Received Date:01 November 2023, Accepted Date:11 December 2023, Published Date:05 January 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: This study aimed to develop an end-to-end system for the diagnosis of breast cancer using a novel combination of a monopole electromagnetic sensor and Convolutional Neural Network (CNN). Methods: The research involved the design and simulation of an electromagnetic sensor, utilizing a denim gene substrate, to capture dielectric changes within breast tissue across a broad spectrum (1GHz to 10GHz). The recorded data was processed by a pre-trained CNN to identify irregularities in the breast's internal structure. Findings: Through extensive simulations, the electromagnetic sensor displayed a remarkable sensitivity to changes in the dielectric properties of breast tissue. The CNN analysis accurately identified the presence of cancer cells and estimated tumor size with an impressive 98% accuracy and a 1% tolerance margin. This method significantly outperformed existing models in both accuracy and efficiency, reducing the need for costly imaging techniques. Novelty: This research offers a non-invasive, cost-effective solution for early-stage breast cancer detection. Unlike traditional imaging techniques, this approach provides accurate diagnostics without the need for extensive equipment or high-cost procedures.

Keywords: Breast Cancer, Electromagnetic Sensor, Convolutional Neural Network, Diagnosis, Noninvasive Diagnosis

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Copyright

© 2024 Prashanthi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)