Breast Cancer Detection using IR-UWB with Deep Learning

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dc.contributor.authorKhumdee, Mawin
dc.contributor.authorAssawaroongsakul, Pongpol
dc.contributor.authorPhasukkit, Pattarapong
dc.contributor.authorHoungkamhang, Nongluck
dc.date.accessioned2026-08-06T10:30:39Z
dc.date.available2026-08-06T10:30:39Z
dc.date.issued2021-01-01
dc.description.abstractThis 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%.
dc.identifier.citation16th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2021, 2021
dc.identifier.doi10.1109/iSAI-NLP54397.2021.9678158
dc.identifier.other2-s2.0-85125314843
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/11516
dc.source16th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2021
dc.subjectBreast Cancer
dc.subjectDeep Neural Network
dc.subjectIR-UWB
dc.titleBreast Cancer Detection using IR-UWB with Deep Learning
dc.typeConference Paper

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