volume | PIER Journals

Abstract

This paper proposes an application of ultra-wideband antenna in conjunction with supervised machine learning to detect the existence of breast tumor. The microstrip line fed octagonal shaped UWB antenna is designed by using Ansys high-frequency structure simulator 2022 R2. It is fabricated on double sided copper FR4 epoxy glass substrate of size 40 mm × 40 mm and tested by using vector network analyzer N9916A. The antenna structure is optimized over the frequency spectrum of 3.1 GHz to 10.6 GHz to obtain minimum value of return loss. The optimized structure provides bandwidth spectrum of 8.38 GHz covering the frequency range of 2.76 to 11.15 GHz with maximum gain of 5.3 dB at 8 GHz. The homogenous artificial breast phantoms with and without tumor are fabricated using different chemical compositions. The dielectric traits of skin, fatty, glandular and tumor layers are analyzed. Microwave sensing for detecting the presence of breast cancer uses the disparity between tumor and breast tissues, requiring consideration of dispersiveness to accurately assess the dielectric characteristics of the breast model due to its lossy dispersive nature. The three sets of reflection characteristics of the entire system comprised of antenna with phantoms are recorded by using VNA with a gap of week to constitute the dataset. The ultrasonic gel serves as a medium for matching between the breast model and antenna. Further, the supervised machine learning approach is used to improve the detection accuracy. Supervised learning, a key category of machine learning, uses labeled data to predict unseen data. The Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest and Multilayer Perceptron algorithms are applied on the measured data to classify the healthy and tumorous tissues. The random forest proven to be best fit on the data with auc score of 98.05%.

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Dr. Sonal Amit Patil

Dr. Ashwini Naik