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    Non-Ionic Deep Learning-Driven IR-UWB Multiantenna Scheme for Breast Tumor Localization
    (2022-01-01)
    Phasukkit, Pattarapong
    This research proposes a deep learning-driven impulse radio ultra-wideband (IR-UWB) multiantenna scheme for non-ionic breast tumor localization. The structure of the multiantenna scheme consists of one side slot Vivaldi transmitting (Tx) and nine side slot Vivaldi receiving (Rx1-Rx9) antennas. To mitigate the attenuation and improve the diagnostic accuracy, the multiantenna scheme is rotated clockwise in 90° increments around the breast, with the angular position of the Tx antenna of 0°, 90°, 180°, and 270°. The deep learning algorithm is utilized to detect and localize the breast tumor, with 17 classification outputs, consisting of classifications 1-16 which correspond to 16 vertically discretized segments of the breast and classification 17 for cancer-free. Experiments were carried out using heterogenous breast replicas with a tumor of 1 cm in diameter, and the breast replicas possess the dielectric property and Hounsfield units (HU) similar to those of human breasts. The experimental results were compared with the computed tomography (CT) scan images. The results reveal that the multiantenna scheme could efficiently detect and accurately localize the breast tumor for nearly all classifications, with the total accuracy (average of F1 scores) of 99.11 %. Specifically, the novelty of this research lies in the use of deep learning with the IR-UWB technology to effectively localize breast tumors.
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    Breast Cancer Detection using IR-UWB with Deep Learning
    (2021-01-01)
    Khumdee, Mawin
    ;
    Assawaroongsakul, Pongpol
    ;
    Phasukkit, Pattarapong
    ;
    Houngkamhang, Nongluck
    This paper proposes breast cancer positioning detection using the IR-UWB system with deep learning, which is an interesting alternative method. When compared to ultrasound, x-ray mammogram, and CT-scan, there are several advantages to using IR-UWB, including low cost, less energy required, less long-term effect, portability, and providing much more breast cancer screening access for patients. Nowadays, the IR-UWB system has many techniques for processing IR-UWB signals, and one of the most interesting technique is using deep learning. In this study, we collected data from nine IR-UWB antennas. Then, the prepared data is fed through Deep Neural Networks to find the hidden patterns of signal and predict the cancer position which are 16 of breast cancer positions and one of undetected, also known as 17 classes. The model gave an average accuracy up to 95.60%.