| Title |
Analysis of a Deep Learning Synchrotron Imaging Model for Segmentation and Classification of Stroke Animal Models |
| Authors |
원혜연(Hyeyeon Won) ; 김수복(Subok Kim) ; 김은빈(Eun Bin Kim) ; 이언석(Onseok Lee) |
| DOI |
https://doi.org/10.5370/KIEE.2023.72.7.863 |
| Keywords |
Stroke; Synchrotron Radiation Imaging; Biomedical; CNN; Deep Learning |
| Abstract |
Stroke causes muscle dysfunction in lower limb depending on the brain damage. Therefore, it is important to identify the degree of muscle damaged and perform rehabilitation training for appropriate time of treatment. However, there is a limitation in that analysis using existing imaging techniques and artificial intelligence cannot analyze disease mechanisms.
This paper aims to develop an AI GUI system using SRI to acquire damaged muscle regions, segment them into fiber and space areas, and classify them. For the segmentation, Attention U-Net performed best accuracy 95.32%. For the classification, ResNet50 with Attention U-Net performed best accuracy 99.07%. As a result of this, we designated the best performing network as suitable for stroke animal models. As an auxiliary tool for diagnosing the degree of stroke muscle damage in clinical practice, we constructed a system to analyze the degree of stroke fiber distribution on SRI images using pixel intensity values to show the results. Through this study, it is system that uses deep learning in the stroke animal model can be applied as a basic study for objective muscle tissue evaluation. |