Breast Tumor Localization Using Simultaneous Perturbation Stochastic-Neural Algorithm

Many impalpable or occult breast cancers cannot be localized using imaging techniques like mam- mography and ultrasound. An accurate localization of the tumor is, however, essential to guide the surgeon to the lesion, and ensure its correct and adequate removal with satisfactory excision margins. Current breast tumor localization techniques are invasive and often result in a cosmetic disfigurement. In this paper, we use the ultrawide band radar-based microwave breast imaging technique to non-invasively localize (impalpable) tumors in the breast. We consider four clinically important lesion features: location, size, depth and spatial orientation within the breast. A comparison of the energy of the received signal from healthy and cancerous breasts exhibits some remarkable differences in some frequency bands. We, therefore, use the energy spectrum of the receiving antenna signal decomposed by wavelet transform as the input to a Simultaneous Perturbation Neural Network (SPNN) classifier. Fur- thermore, we determine the optimum structure and gains of the SPNN, in terms of optimum initial weights and optimum number of nodes in the hidden layer. We use CST Microwave Studio to simulate a data set of 1024 cancer cases with various tumor location, size, depth and direction inside the breast. Our results show that the proposed algorithm gives accurate localization of the breast lesion, and possesses a high sensitivity to small tumor sizes. Additionally, it can accurately detect and classify multiple tumors with short learning and testing time.