Abstract
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.
Highlights
A complete understanding of the distribution of tumor before therapy is a key factor in managing any case of advanced breast cancer
If the tumor shrinks to 50% or more after chemotherapy or if the tumor shrinks to less than 2 cm, the surgeon would need an accurate guide to the lesion in order to remove it from the breast with satisfactory excision margins, and be well tolerated by the patient
Applying the simultaneous perturbation to find the optimal neural network parameters, we found that the optimal number of nodes in the hidden layer is 13
Summary
A complete understanding of the distribution of tumor before therapy is a key factor in managing any case of advanced breast cancer. We use the wavelet decomposition to compress while enhancing the backscattered signal’s signature in the frequency domain, and an optimized simultaneous perturbation neural network for accurate tumor localization. Al-Shenawee’s group [6,7,8] showed the applicability of artificial neural networks to breast cancer detection using radar-based microwave imaging They were able to detect tumors by using an estimate of the dielectric permittivity of the breast. We found that there are remarkable differences in specific frequency bands between healthy and tumor tissues We foster this difference in the frequency domain for tumor localization by using the wavelet transform of the received signal’s energy to obtain a discriminative feature vector for the neural network classifier.
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