volume | PIER Journals

Abstract

This work explores the data-driven approaches for breast tumor detection and analysis of different breast tissues by using microwave sensing technique. Microwave sensing offers a promising trade-off in tissue penetration depth and is prominent dielectric disparity between healthy and tumorous tissues at microwave frequencies. Tumor cells exhibit unique properties, such as increased water content and different ionic composition, which create distinct dielectric traits compared to healthy tissue. This frequently shows variations in loss characteristics compared to normal tissue and can exploit those differences for detection. The key parameter used is Specific Absorption Rate for the determination of tumor location. The differential absorption between healthy and tumor tissue is potentially aided in identifying the presence of lesion. The five sets of reflection characteristics are recorded with the system comprising UWB antenna with breast phantom by using VNA with a gap of 4-5 days. Further, the dimensionality reduction technique is applied to extract the features using PCA and tSNE. In order to enhance the detection accuracy, dimensionality reduction techniques are used in tandem with the supervised machine learning approach. Among the four supervised algorithms, including SVM, KNN, RF and MLP, the random forest was found to be the most optimal for the data with an auc score of 99.97%.

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Dr. Sonal Amit Patil

Dr. Ashwini Naik