volume | PIER Journals

Abstract

This paper presents a novel technique to detect tumors in human breasts using a single high-gain antenna and metasurface (MTS) layer. This design is realized to educate artificial neural networks (ANNs) and deliver productive output. We employ an ANN algorithm to classify detected tumors as healthy, benign, or malignant, based on the permittivity of the detected tissues. The method for finding and sorting things uses the fact of normal and abnormal biological tissues having different dielectric properties, which are based on the tissue's actual permittivity. The study focuses on demonstrating the effectiveness of the proposed technique for the detection and localization of malignant tumors within human breasts. The proposed Vivaldi antenna is made to work over 5 GHz to 9 GHz with a gain of 17.7 dBi at 6.5 GHz and a half-power beamwidth of 10°. The electromagnetic analysis is done using voxel datasets from human models. For this, we located the breast tissue with tumor inside phantom between the antenna structure and the MTS layer. The obtained numerical results from CST MWS are validated experimentally to be used to realize the training of the considered ANNs for tumor detection. The obtained results from the considered ANNs show minimal average errors and high-performance indices for fat thickness, tumor size, and tumor type. The achieved results are found to realize minimum error percentage rate below 2%. The adopted method is found to be very suitable for tumor detection and localization.

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Dr. Raya Adel Kamil

Dr. Noof T. Mahmood

Dr. Zainab Salam Muqdad

Dr. Marwah Haleem Jwair

Dr. Noor Mohammed Noori

Prof. Taha Ahmed Elwi