6/7/2023 0 Comments Caffe finetune googlenetMost existing studies of this issue have mainly focused on the classification of NC, MCI and AD using machine learning techniques ( 3, 4). As therapeutic intervention is likely more beneficial in the early stage of the disease, mild cognitive impairment (MCI), which has a 10% to 15% risk per year to convert into AD compared with normal elderly persons ( 2), has attracted more and more attention. Accepted for publication Oct 31, 2018.Īlzheimer’s disease (AD) is an irreversible, progressive, neurodegenerative disease characterized by global cognitive decline, and behavioral and functional changes, which heavily affects the ability of individuals to perform basic activities of daily life ( 1). Keywords: Mild cognitive impairment (MCI) deep convolutional neural network (deep CNN) classification conversion risk prediction transfer learning Furthermore, the accuracy measures of conversion risk of patients with cMCI ranged from 71.25% to 83.25% in different time points using GoogleNet, whereas the CaffeNet achieved remarkable accuracy measures from 95.42% to 97.01% in conversion risk prediction.Ĭonclusions: The experimental results demonstrated that the proposed methods had prominent capability in classification among the 3 groups such as sMCI, cMCI and NC, and exhibited significant ability in conversion risk prediction of patients with MCI. Results: The GoogleNet acquired accuracies with 97.58%, 67.33% and 84.71% in three-way discrimination among the NC, sMCI and cMCI groups respectively, whereas the CaffeNet obtained promising accuracies of 98.71%, 72.04% and 92.35% in the NC, sMCI and cMCI classifications. A novel data augmentation approach using random views aggregation was applied to generate abundant image patches from the original MR scans. Two CNN architectures including GoogleNet and CaffeNet were explored and evaluated in multiple classifications and estimations of conversion risk using transfer learning from pre-trained ImageNet (via fine-tuning) and five-fold cross-validation. The deep convolutional neural networks (CNNs) were adopted to distinguish different stages of MCI from the NC group, and predict the conversion time from MCI to AD. ![]() Methods: In this study, the baseline MR images and follow-up information during 3 years of 150 normal controls (NC), 150 patients with stable MCI (sMCI) and 157 converted MCI (cMCI) were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Moreover, accurate prediction of the progress and the conversion risk from MCI to probable AD has been of great importance in clinical application. ![]() ![]() Policy of Dealing with Allegations of Research Misconductīackground: Recently, studies have demonstrated that machine learning techniques, particularly cutting-edge deep learning technology, have achieved significant progression on the classification of Alzheimer’s disease (AD) and its prodromal phase, mild cognitive impairment (MCI).Policy of Screening for Plagiarism Process.
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