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.

scholarly journals A Practical Alzheimer Disease Classifier via Brain Imaging-Based Deep Learning on 85,721 Samples

2021 ◽  
Author(s):  
Bin Lu ◽  
Hui-Xian Li ◽  
Zhi-Kai Chang ◽  
Le Li ◽  
Ning-Xuan Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Transfer Learning ◽  
Brain Mri ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Generalization Capability ◽  
Brain Images ◽  
Magnetic Resonance Imaging Mri

Abstract BackgroundBeyond detecting brain lesions or tumors, comparatively little success has been attained in identifying brain disorders such as Alzheimer’s disease (AD), based on magnetic resonance imaging (MRI). Many machine learning algorithms to detect AD have been trained using limited training data, meaning they often generalize poorly when applied to scans from previously unseen populations. Therefore, we built a practical brain MRI-based AD diagnostic classifier using deep learning/transfer learning on dataset of unprecedented size and diversity. MethodsA retrospective MRI dataset pooled from more than 217 sites/scanners constituted the largest brain MRI sample to date (85,721 scans from 50,876 participants) between January 2017 and August 2021. Next, a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, was built as a sex classifier with high generalization capability. The sex classifier achieved 94.9% accuracy and served as a base model in transfer learning for the objective diagnosis of AD. FindingsAfter transfer learning, the model fine-tuned for AD classification achieved 91.3% accuracy in leave-sites-out cross-validation on the Alzheimer's Disease Neuroimaging Initiative (ADNI, 6,857 samples) dataset and 94.2%/93.6%/90.5% accuracy for direct tests on three unseen independent datasets (AIBL, 669 samples / MIRIAD, 644 samples / OASIS, 1,123 samples). When this AD classifier was tested on brain images from unseen mild cognitive impairment (MCI) patients, MCI patients who finally converted to AD were 3 times more likely to be predicted as AD than MCI patients who did not convert (65.2% vs 20.6%). Predicted scores from the AD classifier showed significant correlations with illness severity. InterpretationIn sum, the proposed AD classifier could offer a medical-grade marker that have potential to be integrated into AD diagnostic practice.

scholarly journals The Diagnosis of Alzheimer’s Disease: An Ensemble Approach

2020 ◽  
Author(s):  
Jingyan Qiu ◽  
Linjian Li ◽  
Yida Liu ◽  
Yingjun Ou ◽  
Yubei Lin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Large Scale ◽  
Early Stage ◽  
Training Transfer ◽  
Detection And Diagnosis ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Model ◽  
The Brain

Alzheimer’s disease (AD) is one of the most common forms of dementia. The early stage of the disease is defined as Mild Cognitive Impairment (MCI). Recent research results have shown the prospect of combining Magnetic Resonance Imaging (MRI) scanning of the brain and deep learning to diagnose AD. However, the CNN deep learning model requires a large scale of samples for training. Transfer learning is the key to enable a model with high accuracy by using limited data for training. In this paper, DenseNet and Inception V4, which were pre-trained on the ImageNet dataset to obtain initialization values of weights, are, respectively, used for the graphic classification task. The ensemble method is employed to enhance the effectiveness and efficiency of the classification models and the result of different models are eventually processed through probability-based fusion. Our experiments were completely conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset. Only the ternary classification is made due to a higher demand for medical detection and diagnosis. The accuracies of AD/MCI/Normal Control (NC) of different models are estimated in this paper. The results of the experiments showed that the accuracies of the method achieved a maximum of 92.65%, which is a remarkable outcome compared with the accuracies of the state-of-the-art methods.

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).

scholarly journals Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer’s Disease

2021 ◽  
Vol 14 ◽  
Author(s):  
Jingjing Hu ◽  
Zhao Qing ◽  
Renyuan Liu ◽  
Xin Zhang ◽  
Pin Lv ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Frontotemporal Dementia ◽  
Image Data ◽  
Rapid Acquisition ◽  
Magnetic Resonance Imaging Mri ◽  
The One ◽  
The Right

Frontotemporal dementia (FTD) and Alzheimer’s disease (AD) have overlapping symptoms, and accurate differential diagnosis is important for targeted intervention and treatment. Previous studies suggest that the deep learning (DL) techniques have the potential to solve the differential diagnosis problem of FTD, AD and normal controls (NCs), but its performance is still unclear. In addition, existing DL-assisted diagnostic studies still rely on hypothesis-based expert-level preprocessing. On the one hand, it imposes high requirements on clinicians and data themselves; On the other hand, it hinders the backtracking of classification results to the original image data, resulting in the classification results cannot be interpreted intuitively. In the current study, a large cohort of 3D T1-weighted structural magnetic resonance imaging (MRI) volumes (n = 4,099) was collected from two publicly available databases, i.e., the ADNI and the NIFD. We trained a DL-based network directly based on raw T1 images to classify FTD, AD and corresponding NCs. And we evaluated the convergence speed, differential diagnosis ability, robustness and generalizability under nine scenarios. The proposed network yielded an accuracy of 91.83% based on the most common T1-weighted sequence [magnetization-prepared rapid acquisition with gradient echo (MPRAGE)]. The knowledge learned by the DL network through multiple classification tasks can also be used to solve subproblems, and the knowledge is generalizable and not limited to a specified dataset. Furthermore, we applied a gradient visualization algorithm based on guided backpropagation to calculate the contribution graph, which tells us intuitively why the DL-based networks make each decision. The regions making valuable contributions to FTD were more widespread in the right frontal white matter regions, while the left temporal, bilateral inferior frontal and parahippocampal regions were contributors to the classification of AD. Our results demonstrated that DL-based networks have the ability to solve the enigma of differential diagnosis of diseases without any hypothesis-based preprocessing. Moreover, they may mine the potential patterns that may be different from human clinicians, which may provide new insight into the understanding of FTD and AD.

scholarly journals System for Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 28
Author(s):  
Alejandro Puente-Castro ◽  
Cristian Robert Munteanu ◽  
Enrique Fernandez-Blanco
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Reference Data ◽  
Disease Diagnosis ◽  
Resonance Imaging ◽  
Automatic Assessment ◽  
Learning Techniques ◽  
Magnetic Resonance Imaging Mri

Automatic detection of Alzheimer’s disease is a very active area of research. This is due to its usefulness in starting the protocol to stop the inevitable progression of this neurodegenerative disease. This paper proposes a system for the detection of the disease by means of Deep Learning techniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN) and two sets of reference data for training are proposed. Finally, the goodness of this system is verified within the domain of the application.

scholarly journals Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning

2021 ◽  
Vol 4 (3) ◽  
pp. 49
Author(s):  
Sumit Salunkhe ◽  
Mrinal Bachute ◽  
Shilpa Gite ◽  
Nishad Vyas ◽  
Saanil Khanna ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complex Disease ◽  
Texture Classification ◽  
Region Of Interest ◽  
Structural Data ◽  
Support Vector ◽  
Magnetic Resonance Imaging Mri ◽  
The Many ◽  
Occurrence Matrix

Alzheimer’s disease (AD) has been studied extensively to understand the nature of this complex disease and address the many research gaps concerning prognosis and diagnosis. Several studies based on structural and textural characteristics have already been conducted to aid in identifying AD patients. In this work, an image processing methodology was used to extract textural information and classify the patients into two groups: AD and Cognitively Normal (CN). The Gray Level Co-occurrence Matrix (GLCM) was employed since it is a strong foundation for texture classification. Various textural parameters derived from the GLCM aided in deciphering the characteristics of a Magnetic Resonance Imaging (MRI) region of interest (ROI). Several commonly used image classification algorithms were employed. MATLAB was used to successfully derive 20 features based on the GLCM of the MRI dataset. Based on the data analysis, 8 of the 20 features were determined as significant elements. Ensemble (90.2%), Decision Trees (88.5%), and Support Vector Machine (SVM) (87.2%) were the best performing classifiers. It was observed in GLCM that as the distance (d) between pixels increased, the classification accuracy decreased. The best result was observed for GLCM with d = 1 and direction (d, d, −d) with age and structural data.

scholarly journals Classification of Sex and Alzheimer’s Disease via Brain Imaging-Based Deep Learning on 85,721 Samples

2020 ◽  
Author(s):  
Bin Lu ◽  
Hui-Xian Li ◽  
Zhi-Kai Chang ◽  
Le Li ◽  
Ning-Xuan Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Individual Differences ◽  
Brain Imaging ◽  
Transfer Learning ◽  
Brain Mri ◽  
Classification Model ◽  
Parahippocampal Gyrus ◽  
Industrial Grade

AbstractBeyond detecting brain damage or tumors, little success has been attained on identifying individual differences and brain disorders with magnetic resonance imaging (MRI). Here, we sought to build industrial-grade brain imaging-based classifiers to infer two types of such inter-individual differences: sex and Alzheimer’s disease (AD), using deep learning/transfer learning on big data. We pooled brain structural data from 217 sites/scanners to constitute the largest brain MRI sample to date (85,721 samples from 50,876 participants), and applied a state-of-the-art deep convolutional neural network, Inception-ResNet-V2, to build a sex classifier with high generalizability. In cross-dataset-validation, the sex classification model was able to classify the sex of any participant with brain structural imaging data from any scanner with 94.9% accuracy. We then applied transfer learning based on this model to objectively diagnose AD, achieving 88.4% accuracy in cross-site-validation on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and 91.2% / 86.1% accuracy for a direct test on two unseen independent datasets (AIBL / OASIS). Directly testing this AD classifier on brain images of unseen mild cognitive impairment (MCI) patients, the model correctly predicted 63.2% who eventually converted into AD, versus predicting 22.1% as AD who did not convert into AD during follow-up. Predicted scores of the AD classifier correlated significantly with illness severity. By contrast, the transfer learning framework was unable to achieve practical accuracy for psychiatric disorders. To improve interpretability of the deep learning models, occlusion tests revealed that hypothalamus, superior vermis, thalamus, amygdala and limbic system areas were critical for predicting sex; hippocampus, parahippocampal gyrus, putamen and insula played key roles in predicting AD. Our trained model, code, preprocessed data and an online prediction website have been openly-shared to advance the clinical utility of brain imaging.

scholarly journals The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2091
Author(s):  
Yu-Ching Ni ◽  
Fan-Pin Tseng ◽  
Ming-Chyi Pai ◽  
Ing-Tsung Hsiao ◽  
Kun-Ju Lin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Transfer Learning ◽  
Lewy Body ◽  
Lewy Body Dementia ◽  
Model Complexity ◽  
Patient Treatment ◽  
Data Set ◽  
Spect Images

The correct differential diagnosis of dementia has an important impact on patient treatment and follow-up care strategies. Tc-99m-ECD SPECT imaging, which is low cost and accessible in general clinics, is used to identify the two common types of dementia, Alzheimer’s disease (AD) and Lewy body dementia (LBD). Two-stage transfer learning technology and reducing model complexity based on the ResNet-50 model were performed using the ImageNet data set and ADNI database. To improve training accuracy, the three-dimensional image was reorganized into three sets of two-dimensional images for data augmentation and ensemble learning, then the performance of various deep learning models for Tc-99m-ECD SPECT images to distinguish AD/normal cognition (NC), LBD/NC, and AD/LBD were investigated. In the AD/NC, LBD/NC, and AD/LBD tasks, the AUC values were around 0.94, 0.95, and 0.74, regardless of training models, with an accuracy of 90%, 87%, and 71%, and F1 scores of 89%, 86%, and 76% in the best cases. The use of transfer learning and a modified model resulted in better prediction results, increasing the accuracy by 32% for AD/NC. The proposed method is practical and could rapidly utilize a deep learning model to automatically extract image features based on a small number of SPECT brain perfusion images in general clinics to objectively distinguish AD and LBD.

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