中山醫學大學機構典藏 CSMUIR:Item 310902500/21773
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    题名: Ischemic Stroke Detection System Based on GAN Model
    作者: Ming-Chi Wu ； Chiun-Li Chin ； Ni-Chuan Chung ； Hsiu-Fang Chen ； Kuan-Chun Chen
    贡献者: 中山醫學大學
    关键词: CT image ； Ischemic Stroke ； GAN ； Auto segmentation ； CycleGAN
    日期: 2021-06-01
    上传时间: 2021-09-13T02:56:29Z (UTC)
    出版者: 研發處育成中心暨產學合作組
    摘要: Stroke is a leading cause of death and disability worldwide, with ischemic stroke most common. Brain CT images of patients who have suffered ischemic stroke show obvious features. Hence, brain CT images are often used to determine whether a patient has suffered an ischemic stroke. In recent years, there has been rapid development of software, hardware, and artificial intelligence (AI) applications. To speed up the diagnostic process, medical images can be labeled using AI technology. As medical images are difficult to acquire, we applied Generative Adversarial Network (GAN) deep learning algorithm. Through the network of generators and discriminators in the GAN model, there is continuous learning with improved parameters from a small amount of data. With this model, the best parameters are determined to generate labeled brain CT images that closely match those manually labeled by physicians. The method proposed in this paper can be used to precisely label the regions of ischemic stroke. In addition, we provide a platform for doctors to label brain CT images. We hope that the proposed method can effectively assist physicians in studying brain CT images, thereby shortening the diagnostic time, accelerating the treatment process, and providing an understanding of a patient's condition in the shortest amount of time.
    URI: https://ir.csmu.edu.tw:8080/handle/310902500/21773
    關聯: 中山醫學雜誌, 32卷1期, P11 - 20
    显示于类别:[研發處] 期刊論文

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