Understanding Breast Cancer Detection: A Comprehensive Survey

Abstract

The potentialities of improving the penetration of millimeter waves for breast cancer imaging are here explored. Methods: A field shaping technique based on a convex optimization method is proposed, capable of increasing the field level inside a breast-emulating stratification. Results: The theoretical results are numerically validated via the design and simulation of two circularly polarized antennas. The experimental validation of the designed antennas, using tissue-mimicking phantoms, is provided, being in good agreement with the theoretical predictions. Conclusion: The possibility of focusing, within a lossy medium, the electromagnetic power at millimeter-wave frequencies is demonstrated. Significance: Field shaping can be a key for using millimeter waves for breast cancer detection. Index Terms—Breast Cancer, Microwave Imaging, Millimeter Waves, Radial Line Slot Arrays, Near-Field Optimization. A field focusing technique based on a convex optimization method is proposed, capable of increasing the field level inside a breast-emulating stratification. A focusing technique based on convex optimization has been used here to increase the penetration in breast cancer imaging scenarios at millimeter waves. Significant enhancements of the field level inside a breast model have been achieved by employing focused apertures. The key challenge in cancer detection is how to classify tumors into malignant or benign machine learning techniques can dramatically improves the accuracy of diagnosis. As the methods itself show that it’s much complicated in designing and implementation. The data visualization process itself is a complicated process which can generate false results if the image has noise, so the result won’t be correct. It usually contains two major steps but the further steps are much similar to the above methods ,which makes it further more complicated as it including converting patched images to real images which makes the result inaccurate in diagnosis.