Machine Learning Based Breast Cancer Detection

Abstract: In this study, a novel ultra-wideband (UWB) radar-based method for early, non-invasive breast cancer diagnosis is presented. The system uses a curved seventh derivative Gaussian UWB pulse that is filtered by a sharp transition bandpass FIR filter in both monostatic and bistatic radar configurations. The pulse shape enhances radiation power, spectrum efficiency, and FCC-compliant safety. Tumors are located and detected using Specific Absorption Rate (SAR) measurement and backscattered signal processing. Results from experiments and simulations confirm that the system can accurately identify cancers.

Breast Cancer Detection, Pulse Shaping, Gaussian Derivative Pulse, Monostatic and Bistatic Radar, Specific Absorption Rate (SAR), Breast Phantom, FCC Spectral Mask, FIR Filter, Electromagnetic Imaging

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