scholarly journals Classification of Initial Stages of Alzheimer’s Disease through Pet Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3101
Author(s):  
Ahsan Bin Tufail ◽  
Yong-Kui Ma ◽  
Mohammed K. A. Kaabar ◽  
Ateeq Ur Rehman ◽  
Rahim Khan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Binary Classification ◽  
Image Filtering ◽  
Clinical Settings ◽  
Gaussian Filtering ◽  
3D Cnn ◽  
The Impact

Alzheimer’s disease (AD) is a leading health concern affecting the elderly population worldwide. It is defined by amyloid plaques, neurofibrillary tangles, and neuronal loss. Neuroimaging modalities such as positron emission tomography (PET) and magnetic resonance imaging are routinely used in clinical settings to monitor the alterations in the brain during the course of progression of AD. Deep learning techniques such as convolutional neural networks (CNNs) have found numerous applications in healthcare and other technologies. Together with neuroimaging modalities, they can be deployed in clinical settings to learn effective representations of data for different tasks such as classification, segmentation, detection, etc. Image filtering methods are instrumental in making images viable for image processing operations and have found numerous applications in image-processing-related tasks. In this work, we deployed 3D-CNNs to learn effective representations of PET modality data to quantify the impact of different image filtering approaches. We used box filtering, median filtering, Gaussian filtering, and modified Gaussian filtering approaches to preprocess the images and use them for classification using 3D-CNN architecture. Our findings suggest that these approaches are nearly equivalent and have no distinct advantage over one another. For the multiclass classification task between normal control (NC), mild cognitive impairment (MCI), and AD classes, the 3D-CNN architecture trained using Gaussian-filtered data performed the best. For binary classification between NC and MCI classes, the 3D-CNN architecture trained using median-filtered data performed the best, while, for binary classification between AD and MCI classes, the 3D-CNN architecture trained using modified Gaussian-filtered data performed the best. Finally, for binary classification between AD and NC classes, the 3D-CNN architecture trained using box-filtered data performed the best.

Automatic Diagnosis of Alzheimer’s Disease Using Deep Learning Techniques

2021 ◽  
Vol 19 (11) ◽  
pp. 126-140
Author(s):  
Zahraa S. Aaraji ◽  
Hawraa H. Abbas
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Data Analysis ◽  
Binary Classification ◽  
Brain Mri ◽  
Three Dimensional ◽  
Structural Variations ◽  
Automatic Feature Extraction ◽  
Multi Class Classification

Neuroimaging data analysis has attracted a great deal of attention with respect to the accurate diagnosis of Alzheimer’s disease (AD). Magnetic Resonance Imaging (MRI) scanners have thus been commonly used to study AD-related brain structural variations, providing images that demonstrate both morphometric and anatomical changes in the human brain. Deep learning algorithms have already been effectively exploited in other medical image processing applications to identify features and recognise patterns for many diseases that affect the brain and other organs; this paper extends on this to describe a novel computer aided software pipeline for the classification and early diagnosis of AD. The proposed method uses two types of three-dimensional Convolutional Neural Networks (3D CNN) to facilitate brain MRI data analysis and automatic feature extraction and classification, so that pre-processing and post-processing are utilised to normalise the MRI data and facilitate pattern recognition. The experimental results show that the proposed approach achieves 97.5%, 82.5%, and 83.75% accuracy in terms of binary classification AD vs. cognitively normal (CN), CN vs. mild cognitive impairment (MCI) and MCI vs. AD, respectively, as well as 85% accuracy for multi class-classification, based on publicly available data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI).

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 Deep Learning Image Processing Technique for Early Detection of Alzheimer's Disease

2017 ◽  
Vol 107 ◽  
pp. 85-104
Author(s):  
Raju Anitha ◽  
S. Jyothi ◽  
Venkata Naresh Mandhala ◽  
Debnath Bhattacharyya ◽  
Tai-hoon Kim
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Detection ◽  
Processing Technique ◽  
Image Processing Technique

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.

scholarly journals Deep Learning Framework for Alzheimer’s Disease Diagnosis via 3D-CNN and FSBi-LSTM

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 63605-63618 ◽  
Author(s):  
Chiyu Feng ◽  
Ahmed Elazab ◽  
Peng Yang ◽  
Tianfu Wang ◽  
Feng Zhou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Framework ◽  
3D Cnn ◽  
Alzheimer’S Disease Diagnosis

Incorporating Attention Mechanism in Enhancing Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
Nur Amirah Abd Hamid ◽  
Mohd Ibrahim Shapiai ◽  
Uzma Batool ◽  
Ranjit Singh Sarban Singh ◽  
Muhamad Kamal Mohammed Amin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Transfer Learning ◽  
Binary Classification ◽  
Region Of Interest ◽  
Local Features ◽  
Attention Mechanism ◽  
Medical Evaluation ◽  
Magnetic Resonance Imaging Mri

Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disease that requires attentive medical evaluation. Therefore, diagnosing of AD accurately is crucial to provide the patients with appropriate treatment to slow down the progression of AD as well to facilitate the treatment interventions. To date, deep learning by means of convolutional neural networks (CNNs) has been widely used in diagnosing of AD. There are several well-established CNNs architectures that have been used in the image classification domain for magnetic resonance imaging (MRI) images analysis such as LeNet-5, Inception-V4, VGG-16 and Residual Network. However, these existing deep learning-based methods have lack of ability to be spatial invariance to the input data, due to overlooking some salient local features of the region of interest (ROI) (i.e., hippocampal). In medical image analysis, local features of MRI images are hard to exploit due to the small pixel size of ROI. On the other hand, CNNs requires large dataset sample to perform well, but we have limited number of MRI images to train, thus, leading to overfitting. Therefore, we propose a novel deep learning-based model without pre-processing techniques by incorporating attention mechanism and global average pooling (GAP) layer to VGG-16 architecture to capture the salient features of the MRI image for subtle discriminating of AD and normal control (NC). Also, we utilize transfer learning to surpass the overfitting issue. Experiment is performed on data collected from Open Access Series of Imaging Studies (OASIS) database. The accuracy performance of binary classification (AD vs NC) using proposed method significantly outperforms the existing methods, 12-layered CNNs (trained from scratch) and Inception-V4 (transfer learning) by increasing 1.93% and 3.43% of the accuracy. In conclusion, Attention-GAP model capable of improving and achieving notable classification accuracy in diagnosing AD.

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