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• W2948184028 abstract "Abstract Alzheimer's disease is a neuropsychiatric, progressive, also an irreversible disease. There is not an effective cure for the disease. However, early diagnosis has an important role for treatment planning to delay its progression since the treatments have the most impact at the early stage of the disease. Neuroimages obtained by different imaging techniques (for example, diffusion tensor‐based and magnetic resonance‐based imaging) provide powerful information and help to diagnose the disease. In this work, a deeply supervised and robust method has been developed using three dimensional features to provide objective and accurate diagnosis from magnetic resonance images. The main contributions are (a) a new three dimensional convolutional neural network topology; (b) a new Sobolev gradient‐based optimization with weight values for each decision parameters; (c) application of the proposed topology and optimizer to diagnose Alzheimer's disease; (d) comparisons of the results obtained from the recent techniques that have been implemented for Alzheimer's disease diagnosis. Experimental results and quantitative evaluations indicated that the proposed network model is able to achieve to extract desired features from images and provides automated diagnosis with 98.06% accuracy." @default.
• W2948184028 created "2019-06-14" @default.
• W2948184028 creator A5034636034 @default.
• W2948184028 date "2019-06-19" @default.
• W2948184028 modified "2023-10-17" @default.
• W2948184028 title "Diagnosis of Alzheimer's disease with Sobolev gradient‐based optimization and 3D convolutional neural network" @default.
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• W2948184028 doi "https://doi.org/10.1002/cnm.3225" @default.
• W2948184028 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31166647" @default.
• W2948184028 hasPublicationYear "2019" @default.
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