Detection of Alzheimer’s Disease in Brain MR Images Using Hybrid Local Graph Structure

Alzheimer’s Disease (AD) is a progressive and irreversible neurodegenerative condition, which results in dementia. Mild Cognitive Impairment (MCI) is an intermediate state between normal aging and AD. Instead of traditional questionnaire method, magnetic resonance imaging (MRI) can be used by radiologists to diagnose and screening AD recently, but long acquisition time is not conducive to screening AD and MCI. To solve this problem, we develop a Fasu-Net (Fast Alzheimer’s disease Screening neural network with Undersampled MRI) for AD and MCI clinical classification. The network uses undersampled structural MRI with a shorter acquisition time to improve the screening and diagnosis efficiency of AD. For achieving the best classification result, three axial planes of brain MR images were feed into the Fasu-Net with transfer learning method. The experiment results on undersampled 3D T1-weighted images database (ADNI) show that in the AD versus MCI versus HC (Healthy Controls) classification, the Fasu-Net achieved the accuracy of 91.41%, thus can be a potential method for fast clinical screening of AD.

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Classification of Brain MR Images for the Diagnosis of Alzheimer’s Disease Based on Features Extracted from the Three Main Brain Tissues

Proceedings of the 6th Brazilian Technology Symposium (BTSym’20) - Smart Innovation, Systems and Technologies ◽

10.1007/978-3-030-75680-2_25 ◽

2021 ◽

pp. 212-219

Author(s):

Vitor H. Chaves Cambui ◽

Katia M. Poloni ◽

Ricardo J. Ferrari ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Mr Images ◽

Brain Mr Images ◽

Brain Tissues

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A ROBUST OPTIMIZED FEATURE SET BASED AUTOMATIC CLASSIFICATION OF ALZHEIMER’S DISEASE FROM BRAIN MR IMAGES USING K-NN AND ADABOOST

ICTACT Journal on Image and Video Processing ◽

10.21917/ijivp.2017.0234 ◽

2018 ◽

Vol 8 (3) ◽

pp. 1665-1672 ◽

Cited By ~ 3

Author(s):

Rupali S. Kamathe ◽

Kalyani R. Joshi

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Automatic Classification ◽

Mr Images ◽

Brain Mr Images

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

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666191021123854 ◽

2020 ◽

Vol 16 (1) ◽

pp. 27-35 ◽

Cited By ~ 2

Author(s):

Boo-Kyeong Choi ◽

Nuwan Madusanka ◽

Heung-Kook Choi ◽

Jae-Hong So ◽

Cho-Hee Kim ◽

...

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Convolutional Neural Network ◽

Correction Method ◽

Disease Classification ◽

Mr Images ◽

Hippocampal Region ◽

Brain Mr Images ◽

The Brain

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.

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Alzheimer’s Disease Detection using Machine Learning Techniques in 3D MR Images

2020 National Conference on Emerging Trends on Sustainable Technology and Engineering Applications (NCETSTEA) ◽

10.1109/ncetstea48365.2020.9119923 ◽

2020 ◽

Author(s):

Srinivasan Aruchamy ◽

Amrita Haridasan ◽

Ankit Verma ◽

Partha Bhattacharjee ◽

Sambhu Nath Nandy ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Machine Learning Techniques ◽

Disease Detection ◽

Mr Images ◽

Learning Techniques

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Alzheimer's Disease Classification Based on Multi-feature Fusion

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405614666181012102626 ◽

2019 ◽

Vol 15 (2) ◽

pp. 161-169 ◽

Cited By ~ 3

Author(s):

Nuwan Madusanka ◽

Heung-Kook Choi ◽

Jae-Hong So ◽

Boo-Kyeong Choi

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Fusion ◽

Biological Data ◽

Correct Classification ◽

Mr Images ◽

Correct Classification Rate ◽

Classification Rate ◽

Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

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