Convolutional Neural Network-based MR Image Analysis for Alzheimer’s Disease Classification

Abstract

Background: In this study, we used a convolutional neural network (CNN) to classify Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonance (MR) images of the brain. Materials and Methods: The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR images were matched to the International Consortium for Brain Mapping template (ICBM) using 3D-Slicer software. Using prior knowledge and anatomical annotation label information, the hippocampal region was automatically extracted from the brain MR images. Results: The area of the hippocampus in each image was preprocessed using local entropy minimization with a bi-cubic spline model (LEMS) by an inhomogeneity intensity correction method. To train the CNN model, we separated the dataset into three groups, namely AD/NC, AD/MCI, and MCI/NC. The prediction model achieved an accuracy of 92.3% for AD/NC, 85.6% for AD/MCI, and 78.1% for MCI/NC. Conclusion: The results of this study were compared to those of previous studies, and summarized and analyzed to facilitate more flexible analyses based on additional experiments. The classification accuracy obtained by the proposed method is highly accurate. These findings suggest that this approach is efficient and may be a promising strategy to obtain good AD, MCI and NC classification performance using small patch images of hippocampus instead of whole slide images.

An Exploration: Alzheimer’s Disease Classification Based on Convolutional Neural Network

“…The age-by-age prevalence rate has been growing over the years, and interest in dementia-related research has grown worldwide. AD is one of the most well-known diseases among the old populace, and it confers adverse symptoms of dementia, including problems of memory (like intuition, recollecting, arranging, and judgment) [ 1 ]. The reported incidence rate is around 2 percent of the total at 65 years of age and 35 percent of the total or above at the age of 85.…”

Multi-slice representational learning of convolutional neural network for Alzheimer’s disease classification using positron emission tomography

“…The authors then used those segmentations as input data. Choi et al [10] proposed a CNN method using segmented hippocampus ROI for anatomical information. Many studies have applied 3D CNN to take advantage of whole volume data.…”

Prediction and Modeling of Neuropsychological Scores in Alzheimer’s Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks

“…In the current study, we used artificial neural networks (ANNs) to investigate a non-linear model for AD progression trend based on neuropsychological scores. ANNs have been successfully utilized for various brain imaging applications ( Savioz et al, 2009 ; Choi et al, 2020 ; Wen et al, 2020 ) and have gained increasing interest in diverse applications, such as classification, speech recognition, age modeling, modeling and forecasting extreme events, and even face recognition ( Cole et al, 2017 ; Tuan Tran et al, 2017 ; Duc et al, 2020 ; Chowdhury et al, 2021 ). Moreover, ANN was utilized to provide an effective method for early diagnosis of AD ( Wang et al, 2019 ).…”