Intelligent Microwave System for Breast Cancer Detection

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In recent years, microwaves have emerged as a potential technique for breast cancer detection which avoids the discomfort, risks and costs associated with x-rays and excessive cost of MRI. Early microwave-based detection modalities were based on imaging of the dielectric properties with the goal of correlating high contrast breast inclusions with cancerous tissues. These techniques required the use of a plurality of antennas operating over an ultra-wide range of frequencies. The challenge with these techniques, however, was that a typical female breast is highly heterogeneous and contains a complex network of glandular adipose and fibroconnective tissues. Also, human tissues are highly dispersive (frequency-dependent), thus making the use of wideband antennas extremely difficult. Because of the rich variation in the dielectric properties of breast tissues, we remain convinced that microwave imaging has a strong role to play. To address the two fundamental challenges of earlier microwaves-based methods, we propose to develop near-field antenna probes. The probes will be designed to operate over an exceptionally narrow range of frequencies while maintaining high gain, in sharp contrast to traditional near-field probes. The narrow frequency range will render the frequency-dependent nature of the breast tissues irrelevant. The near-field aspect of the probe, coupled with sub-GHz frequencies, allow significant penetration of low-power non-ionizing microwave energy within the breast. Therefore, the presence of an anomaly will create a differentiated resonance profile since the anomaly is not part of the natural makeup of the breast. For the detection system to be robust and suitable in practice, we consider the heterogeneity of the breast tissue and statistical variation amongst different woman as an integral part of the probe development. Artificial intelligence algorithms in the form of neural networks will be developed to train the probe to achieve maximum detection probability.