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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/139473


    Title: Assessing Ischemic Stroke with Convolutional Image Features in Carotid Color Doppler
    Authors: 羅崇銘
    Lo, Chung-Ming
    Hung, Peng-Hsiang
    Contributors: 圖檔所
    Keywords: Acute ischemic stroke;Carotid ultrasound;Convolutional neural networks
    Date: 2021-08
    Issue Date: 2022-03-28 15:59:01 (UTC+8)
    Abstract: Stroke is a leading cause of disability and death worldwide. Early and accurate recognition of acute stroke is critical for achieving a good prognosis. The novel automated system proposed in this study was based on convolutional neural networks (CNNs), which were used to identify lesion findings on carotid color Doppler (CCD) images in patients with acute ischemic stroke. An image database composed of 1032 CCD images from 106 patients with acute ischemic stroke (549 images) and from 79 normal controls (483 images) was retrospectively analyzed. Taking the consensus of two neuroradiologists as the gold standard, different CNN models with and without transfer learning were evaluated with 10-fold cross-validation. The diagnostic information provided from individual color channels was also explored. AlexNet, which was trained from scratch, achieved an accuracy of 91.67%, a sensitivity of 93.33%, a specificity of 90.20% and an area under the receiver operating characteristic curves (AUC) of 0.9432. Other transferred models achieved accuracies between 77.69% and 83.94%. In channel comparisons, the green channel had the best performance, with an accuracy of 87.50%, a sensitivity of 97.78%, a specificity of 78.43% and an AUC of 0.9507. The proposed CNN architecture, as a computer-aided diagnosis system, suggests using automatic feature extraction from CCD images to predict ischemic stroke. The developed scheme has the potential to provide diagnostic suggestions in clinical use.
    Relation: Ultrasound in Medicine and Biology, Vol.47, No.8, pp.2266-2276
    Data Type: article
    DOI 連結: https://doi.org/10.1016/j.ultrasmedbio.2021.03.038
    DOI: 10.1016/j.ultrasmedbio.2021.03.038
    Appears in Collections:[圖書資訊與檔案學研究所] 期刊論文

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