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Mahjoubi, M.A., Hamida, S., Siani, L.E., Cherradi, B., Abbassi, A.E. and Raihani, A. (2023) Deep Learning for Cerebral Hemorrhage Detection and Classification in Head CT Scans Using CNN. 2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Mohammedia, 18-19 May 2023, 1-8. https://doi.org/10.1109/IRASET57153.2023.10153010

has been cited by the following article:

• TITLE: Non-Invasive Cerebral Hemorrhage Size Determination by Microwave Signal Based on RSSD

  AUTHORS: Dequan Ding, Qinwei Li, Jingyao Liu, Ming Yu, Hang Wu

  KEYWORDS: Human Brain, Bleeding Point, Microwave, Resonance Sparse Decomposition

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.11, November 29, 2024

  ABSTRACT: Aiming at the prediction of the size of human cerebral hemorrhage point, a signal processing method based on Resonance Sparse Decomposition (RSSD) algorithm is proposed to decompose and analyze the microwave echo signal. According to the organizational structure of the human brain, a complete human brain model was established, and bleeding points of different sizes were placed at the same position, and 5 antennas were placed around the model (front, back, left, right, and top). RSSD is performed on the obtained echo signal, and Hilbert envelope analysis is performed on the low resonance component obtained by the decomposition, and then the size of the bleeding point is judged. Using CST and MATLAB to conduct simulation analysis and experiments, it is verified that the proposed method can successfully determine the size of the bleeding point, and the effectiveness and feasibility of the method are proved.