The research paper introduces a deep learning approach for distinguishing between benign and malignant breast tissues using Ultra Wide Band (UWB) microwave imaging. A multi-resonant monopole microstrip patch antenna was crafted and placed on a female breast phantom model containing a tumor. The phantom’s dielectric properties were adjusted to mimic those of benign and malignant tissues. The Specific Absorption Rate (SAR) was analyzed by rotating the antenna at various angles throughout the phantom’s trajectory. Images capturing the SAR distribution over the breast phantom at different antenna resonances were obtained. The popular Convolutional Neural Network (CNN) based AlexNet model was employed for autonomous feature learning from SAR images. To address the challenge of overfitting with a limited dataset, the study utilized image augmentation and transfer learning methods. The proposed model achieved an impressive 98.59% accuracy in classifying benign and malignant breast phantom images, surpassing the performance of three other CNN models—VggNet16, GoogleNet, and ResNet. Notably, this microwave imaging system based on SAR images detected tumors as small as 1 mm with an accuracy of 97.08% outperforming existing malignancy screening techniques.
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