An experimental analysis of different Deep Learning based Models for Alzheimer’s Disease classification using Brain Magnetic Resonance Images

Hazarika, Ruhul Amin; Kandar, Debdatta; Maji, Arnab Kumar

doi:10.1016/j.jksuci.2021.09.003

Cited by 39 publications

(22 citation statements)

References 34 publications

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“…We provide the experimental results for one five-way benchmark multiclass classification problem [18], one four-way multiclass classification problem [33], and one three-way classification problem [45]. The suggested model's training efficiency was evaluated in terms of important parameters, i.e., training accuracy, validation accuracy, training loss, and validation loss at different epochs without dropout, with dropout, with dropout and weight decay.…”

Section: Resultsmentioning

confidence: 99%

“…In another study, the authors of [45] utilized different pre-trained models using a fine-tuned approach to transfer learning in the ADNI dataset for three-way AD classification (AD, MCI, and NC). The experimental results showed that DenseNet outperformed the others, achieving a maximal average accuracy of 99.05%.…”

Section: Proposed Randomized Concatenated Deep Features Approachmentioning

confidence: 99%

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An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Odusami

Maskeliūnas

Damaševičius

2022

Sensors

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Alzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD.

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Randomized Concatenated Deep Features Approachmentioning

confidence: 99%

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Odusami

Maskeliūnas

Damaševičius

2022

Sensors

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“…Studies with deep learning neural networks produced a maximum accuracy of 98.3% [ 35 , 36 , 37 ]. As shown in [ 38 ], the authors used neural network modeling to verify performance, and their results showed that DenseNet-121 generated accuracy of 90.22%, which is higher than Inception-V1, V2, and Residual Networks [ 39 ]. A simple classification model based on a decision tree with hyperparameter tuning produced 99% accuracy [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

“…ANN [ 43 ] and RNN [ 62 ] were used in two studies, with results of 96.66% and 81%, respectively. Three of the remaining studies compared multiple DL models [ 38 , 44 , 65 ], with accuracy ranging from 59.8% to 98.86%. Two studies were associated with both ML and DL classifiers [ 38 , 64 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

et al. 2022

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Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.

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“…In the literature, several studies have been proposed for both AD-NC classification and MCI-to-AD prediction tasks using an iterative sparse and DL model [26], for classifying subjects into AD, MCI and NC classes using stacked denoising auto-encoders, 3D-CNNs, support vector machines (SVM), random forests, decision trees, and k-nearest neighbor classifiers [27], for AD subject classification using the gene subset from the DNA methylation dataset and enhanced deep recurrent neural network [28], for AD-NC classification using cerebral catheter angiogram neuroimages and a combination of inception version 3 and densenet201 architectures [29], as well as utilizing dysregulation patterns of miRNA biomarkers for the prediction of AD [30]. They also proposed methods for the classification of frontotemporal dementia, AD and NC using the MRI neuroimaging modality and DL models [31], a novel dense CNN network to differentiate among stable and progressive MCI classes using hippocampal morphometry [32], a U-Net styled DL architecture for AD-NC classification task from retinal vasculature images [33], an explainable 3D residual attention deep neural network (DNN) for AD-NC and progressive MCI-static MCI classification tasks [34], a multi-modal data platform architecture to implement regression tasks and to predict the progression of AD [35], as well as transfer learning models such as LeNet, AlexNet, VGG-16, VGG-19, Inception-V1, Inception-V2, Inception-V3, DenseNet-121, etc., for binary classification between NC, MCI and AD classes [36]. Similarly, research has been done to propose a deep separable CNN model along with AlexNet and GoogLeNet transfer learning based models for AD early diagnosis [37], aggregation of CNN with a deep neural network model for AD-NC classification task [38], gait-based cognitive screening and machine learning to differentiate among AD, MCI and NC classes [39], a variant of CNN for AD, NC, MCI, early MCI and late MCI classification [40], as well as an end-to-end framework comprising of CNNs and MRI scans for AD-NC binary and for multiclass tasks [41].…”

Section: Introductionmentioning

confidence: 99%

Early-Stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains

Tufail

Anwar

Othman

et al. 2022

Sensors

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Alzheimer’s Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD.

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An experimental analysis of different Deep Learning based Models for Alzheimer’s Disease classification using Brain Magnetic Resonance Images

Cited by 39 publications

References 34 publications

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

Early-Stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains

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