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- W4387271230 abstract "The proposed work has utilized a machine learning-based microwave imaging technique to detect the cancerous tumour present in the breast tissue. Microwave signal has been considered for the analysis as it can penetrate deep into the highly dense breast tissue. The paper has presented a breast tumour detection model using ANSYS High Frequency Structure Simulator. Two identical microstrip patch antennas have been designed at 2.45 GHz. A breast model has been simulated, and the antennas are placed diametrically opposite around the breast model. Electromagnetic waves emitted from the transmitting antenna are scattered due to variations in relative permittivity and conductivity of breast tissue layers. The scattering parameters return and transmission losses have been exploited in the time and frequency domain for the classification of the healthy and cancerous tissues present in the breast model using the Random Forest classifier. The experiment offers good accuracy and precision for the detection." @default.
- W4387271230 created "2023-10-03" @default.
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- W4387271230 date "2023-01-01" @default.
- W4387271230 modified "2023-10-03" @default.
- W4387271230 title "Machine Learning Approach Towards the Breast Cancer Detection with Microwave Imaging" @default.
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- W4387271230 doi "https://doi.org/10.1007/978-981-99-4717-1_16" @default.
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