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    Deep Neural Networks for the Qualitative Analysis of Myocardial Perfusion Emission Computed Tomography Images
    (2023-01-01)
    Pruthipanyasakul, Nareekarn
    ;
    Kanungsukkasem, Nont
    ;
    Urruty, Thierry
    ;
    Leelanupab, Teerapong
    Integrating AI into medical diagnosis can provide a more accurate diagnosis when medical staff make treatment decisions. This paper studied on several deep neural networks, re-used with further training for a specific task in classifying the stenosis of a patient's coronary artery. From a 4DM-SPECT application, we collected polar map images that report, for example, myocardial perfusion, function and defect severity from cardiac emission computed tomography examination. We conducted a comparative study to identify the optimal combination of various state-of-the-art pre-trained models (i.e., VGG19, ResNet50, DenseNet121, and EfficientNetB0-B3) and eight different modalities of the myocardial perfusion images for classifying the stenosis of the coronary artery.
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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%.