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題名 Assessing Ischemic Stroke with Convolutional Image Features in Carotid Color Doppler
作者 羅崇銘
Lo, Chung-Ming
Hung, Peng-Hsiang
貢獻者 圖檔所
關鍵詞 Acute ischemic stroke;Carotid ultrasound;Convolutional neural networks
日期 2021-08
上傳時間 28-三月-2022 15:59:01 (UTC+8)
摘要 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.
關聯 Ultrasound in Medicine and Biology, Vol.47, No.8, pp.2266-2276
資料類型 article
DOI https://doi.org/10.1016/j.ultrasmedbio.2021.03.038
dc.contributor 圖檔所
dc.creator (作者) 羅崇銘
dc.creator (作者) Lo, Chung-Ming
dc.creator (作者) Hung, Peng-Hsiang
dc.date (日期) 2021-08
dc.date.accessioned 28-三月-2022 15:59:01 (UTC+8)-
dc.date.available 28-三月-2022 15:59:01 (UTC+8)-
dc.date.issued (上傳時間) 28-三月-2022 15:59:01 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139473-
dc.description.abstract (摘要) 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.
dc.format.extent 146 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Ultrasound in Medicine and Biology, Vol.47, No.8, pp.2266-2276
dc.subject (關鍵詞) Acute ischemic stroke;Carotid ultrasound;Convolutional neural networks
dc.title (題名) Assessing Ischemic Stroke with Convolutional Image Features in Carotid Color Doppler
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1016/j.ultrasmedbio.2021.03.038
dc.doi.uri (DOI) https://doi.org/10.1016/j.ultrasmedbio.2021.03.038