Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process

2020 ◽  
Vol 30 (06) ◽  
pp. 2050032
Author(s):  
Wei Feng ◽  
Nicholas Van Halm-Lutterodt ◽  
Hao Tang ◽  
Andrew Mecum ◽  
Mohamed Kamal Mesregah ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Disease Process ◽  
Disease Classification ◽  
Support Vector ◽  
Imaging Data ◽  
Clinical Tool ◽  
3D Cnn

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.

scholarly journals Multimodal deep learning models for early detection of Alzheimer’s disease stage

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Janani Venugopalan ◽  
Li Tong ◽  
Hamid Reza Hassanzadeh ◽  
May D. Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Verbal Learning ◽  
Disease Stage ◽  
Support Vector ◽  
Clinical Test ◽  
Nucleotide Polymorphisms ◽  
Imaging Data ◽  
Holistic View

AbstractMost current Alzheimer’s disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. The fusion of multiple data modalities can provide a holistic view of AD staging analysis. Thus, we use deep learning (DL) to integrally analyze imaging (magnetic resonance imaging (MRI)), genetic (single nucleotide polymorphisms (SNPs)), and clinical test data to classify patients into AD, MCI, and controls (CN). We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data. We also develop a novel data interpretation method to identify top-performing features learned by the deep-models with clustering and perturbation analysis. Using Alzheimer’s disease neuroimaging initiative (ADNI) dataset, we demonstrate that deep models outperform shallow models, including support vector machines, decision trees, random forests, and k-nearest neighbors. In addition, we demonstrate that integrating multi-modality data outperforms single modality models in terms of accuracy, precision, recall, and meanF1 scores. Our models have identified hippocampus, amygdala brain areas, and the Rey Auditory Verbal Learning Test (RAVLT) as top distinguished features, which are consistent with the known AD literature.

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

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Han Woong Kim ◽  
Ha Eun Lee ◽  
KyeongTaek Oh ◽  
Sangwon Lee ◽  
Mijin Yun ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Positron Emission Tomography ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
External Validation ◽  
Disease Classification ◽  
Emission Tomography ◽  
Learning Methods ◽  
Positron Emission

Abstract Background Alzheimer’s Disease (AD) is a degenerative brain disorder that often occurs in people over 65 years old. As advanced AD is difficult to manage, accurate diagnosis of the disorder is critical. Previous studies have revealed effective deep learning methods of classification. However, deep learning methods require a large number of image datasets. Moreover, medical images are affected by various environmental factors. In the current study, we propose a deep learning-based method for diagnosis of Alzheimer’s disease (AD) that is less sensitive to different datasets for external validation, based upon F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT). Results The accuracy, sensitivity, and specificity of our proposed network were 86.09%, 80.00%, and 92.96% (respectively) using our dataset, and 91.02%, 87.93%, and 93.57% (respectively) using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We observed that our model classified AD and normal cognitive (NC) cases based on the posterior cingulate cortex (PCC), where pathological changes occur in AD. The performance of the GAP layer was considered statistically significant compared to the fully connected layer in both datasets for accuracy, sensitivity, and specificity (p < 0.01). In addition, performance comparison between the ADNI dataset and our dataset showed no statistically significant differences in accuracy, sensitivity, and specificity (p > 0.05). Conclusions The proposed model demonstrated the effectiveness of AD classification using the GAP layer. Our model learned the AD features from PCC in both the ADNI and Severance datasets, which can be seen in the heatmap. Furthermore, we showed that there were no significant differences in performance using statistical analysis.

Deep Learning for Alzheimer’s Disease Classification using Texture Features

2019 ◽  
Vol 15 (7) ◽  
pp. 689-698 ◽  
Author(s):  
Jae-Hong So ◽  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Boo-Kyeong Choi ◽  
Hyeon-Gyun Park
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Image Data ◽  
Magnetic Resonance Images ◽  
Disease Classification ◽  
Support Vector ◽  
K Nearest Neighbor

Background: We propose a classification method for Alzheimer’s disease (AD) based on the texture of the hippocampus, which is the organ that is most affected by the onset of AD. Methods: We obtained magnetic resonance images (MRIs) of Alzheimer’s patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This dataset consists of image data for AD, mild cognitive impairment (MCI), and normal controls (NCs), classified according to the cognitive condition. In this study, the research methods included image processing, texture analyses, and deep learning. Firstly, images were acquired for texture analyses, which were then re-spaced, registered, and cropped with Gabor filters applied to the resulting image data. In the texture analyses, we applied the 3-dimensional (3D) gray-level co-occurrence (GLCM) method to evaluate the textural features of the image, and used Fisher’s coefficient to select the appropriate features for classification. In the last stage, we implemented a deep learning multi-layer perceptron (MLP) model, which we divided into three types, namely, AD-MCI, AD-NC, and MCI-NC. Results: We used this model to assess the accuracy of the proposed method. The classification accuracy of the proposed deep learning model was confirmed in the cases of AD-MCI (72.5%), ADNC (85%), and MCI-NC (75%). We also evaluated the results obtained using a confusion matrix, support vector machine (SVM), and K-nearest neighbor (KNN) classifier and analyzed the results to objectively verify our model. We obtained the highest accuracy of 85% in the AD-NC. Conclusion: The proposed model was at least 6–19% more accurate than the SVM and KNN classifiers, respectively. Hence, this study confirms the validity and superiority of the proposed method, which can be used as a diagnostic tool for early Alzheimer’s diagnosis.

A Comparative Study of Alzheimer Detection Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2877-2883
Author(s):  
Adwait Patil
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Comparative Study ◽  
Fuzzy System ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

Lightweight deep residual network for alzheimer’s disease classification using sMRI slices

2021 ◽  
pp. 1-9
Author(s):  
Yanteng Zhang ◽  
Qizhi Teng ◽  
Linbo Qing ◽  
Yan Liu ◽  
Xiaohai He
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Computational Cost ◽  
Disease Classification ◽  
Learning Networks ◽  
Feature Maps ◽  
Medical Field ◽  
Widespread Application ◽  
Healthy Control

Alzheimer’s disease (AD) is a degenerative brain disease and the most common cause of dementia. In recent years, with the widespread application of artificial intelligence in the medical field, various deep learning-based methods have been applied for AD detection using sMRI images. Many of these networks achieved AD vs HC (Healthy Control) classification accuracy of up to 90%but with a large number of computational parameters and floating point operations (FLOPs). In this paper, we adopt a novel ghost module, which uses a series of cheap operations of linear transformation to generate more feature maps, embedded into our designed ResNet architecture for task of AD vs HC classification. According to experiments on the OASIS dataset, our lightweight network achieves an optimistic accuracy of 97.92%and its total parameters are dozens of times smaller than state-of-the-art deep learning networks. Our proposed AD classification network achieves better performance while the computational cost is reduced significantly.

scholarly journals Comparison of machine learning approaches for enhancing Alzheimer’s disease classification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Spatial Information ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Disease Classification ◽  
Support Vector ◽  
The Brain

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

scholarly journals Deep learning detection of informative features in tau PET for Alzheimer’s disease classification

2020 ◽  
Vol 21 (S21) ◽  
Author(s):  
Taeho Jo ◽  
◽  
Kwangsik Nho ◽  
Shannon L. Risacher ◽  
Andrew J. Saykin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Deep Learning ◽  
Early Detection ◽  
Medial Temporal Lobe ◽  
Memory Loss ◽  
Disease Classification ◽  
Classification Model ◽  
Tau Pet

Abstract Background Alzheimer’s disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans. Results The 3D convolutional neural network (CNN)-based classification model of AD from cognitively normal (CN) yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The layer-wise relevance propagation (LRP) results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r = 0.43 for early MCI and r = 0.49 for late MCI). Conclusion A deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.

scholarly journals Deep learning detection of informative features in tau PET for Alzheimer’s disease classification

2020 ◽  
Author(s):  
Taeho Jo ◽  
Kwangsik Nho ◽  
Shannon L. Risacher ◽  
Andrew J. Saykin ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Deep Learning ◽  
Early Detection ◽  
Group Analysis ◽  
Statistical Parametric Mapping ◽  
Classification Model ◽  
Tau Pet ◽  
3D Cnn

AbstractBackgroundAlzheimer’s disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans.MethodsWe analysed [18F]flortaucipir PET image data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. We first developed an image classifier to distinguish AD from cognitively normal (CN) older adults by training a 3D convolutional neural network (CNN)-based deep learning model on tau PET images (N=132; 66 CN and 66 AD), then applied the classifier to images from individuals with mild cognitive impairment (MCI; N=168). In addition, we applied a layer-wise relevance propagation (LRP)-based model to identify informative features and to visualize classification results. We compared these results with those from whole brain voxel-wise between-group analysis using conventional Statistical Parametric Mapping (SPM12).ResultsThe 3D CNN-based classification model of AD from CN yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The LRP results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r=0.43 for early MCI and r=0.49 for late MCI).ConclusionA deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.

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