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Comparison of Machine-Learning Classification Models for Glaucoma Management
المؤلفون المشاركون
Nakazawa, Toru
Shiga, Yukihiro
Omodaka, Kazuko
Akiba, Masahiro
An, Guangzhou
Tsuda, Satoru
Takada, Naoko
Kikawa, Tsutomu
Yokota, Hideo
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-06-19
دولة النشر
مصر
عدد الصفحات
8
التخصصات الرئيسية
الملخص EN
This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management.
In this study, 163 glaucoma eyes were labelled with four optic disc types by three glaucoma specialists and then randomly separated into training and test data.
All the images of these eyes were captured using optical coherence tomography and laser speckle flowgraphy to quantify the ocular structure and blood-flow-related parameters.
A total of 91 parameters were extracted from each eye along with the patients’ background information.
Machine-learning classifiers, including the neural network (NN), naïve Bayes (NB), support vector machine (SVM), and gradient boosted decision trees (GBDT), were trained to build the classification models, and a hybrid feature selection method that combines minimum redundancy maximum relevance and genetic-algorithm-based feature selection was applied to find the most valid and relevant features for NN, NB, and SVM.
A comparison of the performance of the three machine-learning classification models showed that the NN had the best classification performance with a validated accuracy of 87.8% using only nine ocular parameters.
These selected quantified parameters enabled the trained NN to classify glaucomatous optic discs with relatively high performance without requiring color fundus images.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
An, Guangzhou& Omodaka, Kazuko& Tsuda, Satoru& Shiga, Yukihiro& Takada, Naoko& Kikawa, Tsutomu…[et al.]. 2018. Comparison of Machine-Learning Classification Models for Glaucoma Management. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1187535
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
An, Guangzhou…[et al.]. Comparison of Machine-Learning Classification Models for Glaucoma Management. Journal of Healthcare Engineering No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1187535
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
An, Guangzhou& Omodaka, Kazuko& Tsuda, Satoru& Shiga, Yukihiro& Takada, Naoko& Kikawa, Tsutomu…[et al.]. Comparison of Machine-Learning Classification Models for Glaucoma Management. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1187535
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1187535
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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