کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11008011 | 1840489 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer's disease
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In healthcare sector, it is of crucial importance to accurately diagnose Alzheimer's disease (AD) and its prophase called mild cognitive impairment (MCI) so as to prevent degeneration and provide early treatment for AD patients. In this paper, a framework is proposed for the diagnosis of AD, which consists of MRI images preprocessing, feature extraction, principal component analysis, and the support vector machine (SVM) model. In particular, a new switching delayed particle swarm optimization (SDPSO) algorithm is proposed to optimize the SVM parameters. The developed framework based on the SDPSO-SVM model is successfully applied to the classification of AD and MCI using MRI scans from ADNI dataset. Our developed algorithm can achieve excellent classification accuracies for 6 typical cases. Furthermore, experiment results demonstrate that the proposed algorithm outperforms several SVM models and also two other state-of-art methods with deep learning embedded, thereby serving as an effective AD diagnosis method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 320, 3 December 2018, Pages 195-202
Journal: Neurocomputing - Volume 320, 3 December 2018, Pages 195-202
نویسندگان
Nianyin Zeng, Hong Qiu, Zidong Wang, Weibo Liu, Hong Zhang, Yurong Li,