Alzheimer's Disease stage identification using deep learning models

Predicting poor performance on cognitive tests among older adults using wearable device data and machine learning: a feasibility study

Timely implementation of interventions to slow cognitive decline among older adults requires accurate monitoring to detect changes in cognitive function. Factors known to be associated with cognition that can be gathered from accelerometers, user interfaces, and other sensors within wearable devices could be used to train machine learning models and develop wearable-based cognitive monitoring systems. Using data from over 2400 older adults in the National Health and Nutrition Examination Survey (NHANES) we developed prediction models to differentiate older adults with normal cognition from those with poor cognition based on outcomes from three cognitive tests measuring different domains of cognitive function. During repeated cross-validation CatBoost, XGBoost, and Random Forest models performed best when predicting poor cognition based on tests measuring processing speed, working memory, and attention (median AUCs ≥0.82) compared to immediate and delayed recall (median AUCs ≥0.72) and categorical verbal fluency (median AUC ≥ 0.68). Activity and sleep parameters were also more strongly associated with poor cognition based on tests assessing processing speed, working memory, and attention compared to other cognitive subdomains. Our work provides proof of concept that data collatable through wearable devices such as age, education, sleep parameters, activity summaries, and light exposure metrics could be used to differentiate between older adults with normal versus poor cognition. We further identified metrics that could be targets in future causal studies seeking to better understand how sleep and activity parameters influence cognitive function among older adults.

CLADSI: Deep Continual Learning for Alzheimer's Disease Stage Identification Using Accelerometer Data

Alzheimer's disease (AD) is a neurodegenerative disorder that can cause a significant impairment in physical and cognitive functions. Gait disturbances are also reported as a symptom of AD. Previous works have used Convolutional Neural Networks (CNNs) to analyze data provided by motion sensors that monitor Alzheimer's patients. However, these works have not explored continual learning algorithms that allow the CNN to configure itself as it receives new data from these sensors. This work proposes a method aimed at enabling CNNs to learn from a continuous stream of data from motion sensors without having full access to previous data. The CNN identifies the stage of AD from the analysis of data provided by motion sensors. The work includes an experimentation with data captured by accelerometers that monitored the activity of 35 Alzheimer's patients for a week in a daycare center. The CNN achieves an accuracy of 86,94%, 86,48% and 84,37% for 2, 3 and 4 experiences respectively. The proposal provides advantages to working with a continuous stream of data so that the CNN are constantly self-configuring without the intervention of a human. The work can be considered as promising and helpful in finding deep learning solutions in medical cases in which patients are constantly monitored.

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study

BACKGROUND

Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach.

OBJECTIVE

We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers.

METHODS

The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls.

RESULTS

The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%).

CONCLUSIONS

The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

Predicting mild cognitive impairment in older adults: A machine learning analysis of the Alzheimer's Disease Neuroimaging Initiative

AIM

Mild cognitive impairment (MCI) in older adults is potentially devastating, but an accurate prediction model is still lacking. We hypothesized that neuropsychological tests and MRI-related markers could predict the onset of MCI early.

METHODS

We analyzed data from 306 older adults who were cognitive normal (CN) attending the Alzheimer's Disease Neuroimaging Initiative sequentially (474 pairs of visits) within 3 years. There were 231 pairs of MCI conversion (CN to MCI), and 242 pairs of CN maintenance (CN to CN). Variables on demographic, neuropsychological tests, genetic, and MRI-related markers were collected. Machine learning was used to construct MCI prediction models, comparing the area under the receiver operating characteristic curve (AUC) as the primary metric of performance. Important predictors were ranked for the optimal model.

RESULTS

The baseline age of the study sample was 74.8 years old. The best-performing model (gradient boosting decision tree) with 13 variables predicted MCI with an AUC of 0.819, and the rank of variable importance showed that intracranial volume, hippocampal volume, and score from task 4 (word recognition) of the Alzheimer's Disease Assessment Scale were important predictors of MCI.

CONCLUSIONS

With the help of machine learning, fewer neuropsychological tests and MRI-related markers are required to accurately predict MCI within 3 years, thereby facilitating targeted intervention. Geriatr Gerontol Int 2024; 24: 96-101.

Alzheimer's Disease in Lebanon: Exploring Genetic and Environmental Risk Factors-A Comprehensive Review

Alzheimer's disease (AD) is a neurodegenerative condition that displays a high prevalence in Lebanon causing a local burden in healthcare and socio-economic sectors. Unfortunately, the lack of prevalence studies and clinical trials in Lebanon minimizes the improvement of AD patient health status. In this review, we include over 155 articles to cover the different aspects of AD ranging from mechanisms to possible treatment and management tools. We highlight some important modifiable and non-modifiable risk factors of the disease including genetics, age, cardiovascular diseases, smoking, etc. Finally, we propose a hypothetical genetic synergy model between APOE4 and TREM2 genes which constitutes a potential early diagnostic tool that helps in reducing the risk of AD based on preventative measures decades before cognitive decline. The studies on AD in Lebanon and the Middle East are scarce. This review points out the importance of genetic mapping in the understanding of disease pathology which is crucial for the emergence of novel diagnostic tools. Hence, we establish a rigid basis for further research to identify the most influential genetic and environmental risk factors for the purpose of using more specific diagnostic tools and possibly adopting a local management protocol.

Behavioral marker-based predictive modeling of functional status for older adults with subjective cognitive decline and mild cognitive impairment: Study protocol

Objective

This study describes a research protocol for a behavioral marker-based predictive model that examines the functional status of older adults with subjective cognitive decline and mild cognitive impairment.

Methods

A total of 130 older adults aged ≥65 years with subjective cognitive decline or mild cognitive impairment will be recruited from the Dementia Relief Centers or the Community Service Centers. Data on behavioral and psychosocial markers (e.g. physical activity, mobility, sleep/wake patterns, social interaction, and mild behavioral impairment) will be collected using passive wearable actigraphy, in-person questionnaires, and smartphone-based ecological momentary assessments. Two follow-up assessments will be performed at 12 and 24 months after baseline. Mixed-effect machine learning models: MErf, MEgbm, MEmod, and MEctree, and standard machine learning models without random effects [random forest, gradient boosting machine] will be employed in our analyses to predict functional status over time.

Results

The results of this study will be fundamental for developing tailored digital interventions that apply deep learning techniques to behavioral data to predict, identify, and aid in the management of functional decline in older adults with subjective cognitive decline and mild cognitive impairment. These older adults are considered the optimal target population for preventive interventions and will benefit from such tailored strategies.

Conclusions

Our study will contribute to the development of self-care interventions that utilize behavioral data and machine learning techniques to provide automated analyses of the functional decline of older adults who are at risk for dementia.

Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data

Alzheimer's disease is a genetically complex disorder, and microarray technology provides valuable insights into it. However, the high dimensionality of microarray datasets and small sample sizes pose challenges. Gene selection techniques have emerged as a promising solution to this challenge, potentially revolutionizing AD diagnosis. The study aims to investigate deep learning techniques, specifically neural networks, in predicting Alzheimer's disease using microarray gene expression data. The goal is to develop a reliable predictive model for early detection and diagnosis, potentially improving patient care and intervention strategies. This study employed gene selection techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), to pinpoint pertinent genes within microarray datasets. Leveraging deep learning principles, we harnessed a Convolutional Neural Network (CNN) as our classifier for Alzheimer's disease (AD) prediction. Our approach involved the utilization of a seven-layer CNN with diverse configurations to process the dataset. Empirical outcomes on the AD dataset underscored the effectiveness of the PCA-CNN model, yielding an accuracy of 96.60% and a loss of 0.3503. Likewise, the SVD-CNN model showcased remarkable accuracy, attaining 97.08% and a loss of 0.2466. These results accentuate the potential of our method for gene dimension reduction and classification accuracy enhancement by selecting a subset of pertinent genes. Integrating gene selection methodologies with deep learning architectures presents a promising framework for elevating AD prediction and promoting precision medicine in neurodegenerative disorders. Ongoing research endeavors aim to generalize this approach for diverse applications, explore alternative gene selection techniques, and investigate a variety of deep learning architectures.

Fine-Grained and Multiple Classification for Alzheimer's Disease With Wavelet Convolution Unit Network

In this article, we propose a novel wavelet convolution unit for the image-oriented neural network to integrate wavelet analysis with a vanilla convolution operator to extract deep abstract features more efficiently. On one hand, in order to acquire non-local receptive fields and avoid information loss, we define a new convolution operation by composing a traditional convolution function and approximate and detailed representations after single-scale wavelet decomposition of source images. On the other hand, multi-scale wavelet decomposition is introduced to obtain more comprehensive multi-scale feature information. Then, we fuse all these cross-scale features to improve the problem of inaccurate localization of singular points. Given the novel wavelet convolution unit, we further design a network based on it for fine-grained Alzheimer's disease classifications (i.e., Alzheimer's disease, Normal controls, early mild cognitive impairment, late mild cognitive impairment). Up to now, only a few methods have studied one or several fine-grained classifications, and even fewer methods can achieve both fine-grained and multi-class classifications. We adopt the novel network and diffuse tensor images to achieve fine-grained classifications, which achieved state-of-the-art accuracy for all eight kinds of fine-grained classifications, up to 97.30%, 95.78%, 95.00%, 94.00%, 97.89%, 95.71%, 95.07%, 93.79%. In order to build a reference standard for Alzheimer's disease classifications, we actually implemented all twelve coarse-grained and fine-grained classifications. The results show that the proposed method achieves solidly high accuracy for them. Its classification ability greatly exceeds any kind of existing Alzheimer's disease classification method.

Joint regression modelling of intensity and timing of accelerometer counts

Accelerometers are commonly used in human medical and public health research to measure physical movement, which is relevant in a wide range of studies, from physical activity and sleep behaviours studies, to identification of movement patterns in people affected by diseases of the locomotor system and prediction of risk of injury in high performance sports. The accelerometer output provides the intensity (activity count) and timing (timestamp) of the movement, which can be used to define bouts of activity (periods of sustained movement of a given intensity). In some contexts, it may be important to include both dimensions to obtain a broader and deeper understanding of the phenomenon under study. Such is the case of a large-scale epidemiological investigation on the daily and weekly physical activity behaviours of school-aged children enrolled in the UK Millennium Cohort Study, which has motivated the present article. I present a statistical approach to joint modelling of intensity and timing of activity bouts that takes advantage of the circular nature of the timing. The model, which accounts for the longitudinal structure of the observations, is remarkably simple to implement using standard statistical software.

Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score

Several recent studies indicate that atypical changes in driving behaviors appear to be early signs of mild cognitive impairment (MCI) and dementia. These studies, however, are limited by small sample sizes and short follow-up duration. This study aims to develop an interaction-based classification method building on a statistic named Influence Score (i.e., I-score) for prediction of MCI and dementia using naturalistic driving data collected from the Longitudinal Research on Aging Drivers (LongROAD) project. Naturalistic driving trajectories were collected through in-vehicle recording devices for up to 44 months from 2977 participants who were cognitively intact at the time of enrollment. These data were further processed and aggregated to generate 31 time-series driving variables. Because of high dimensional time-series features for driving variables, we used I-score for variable selection. I-score is a measure to evaluate variables' ability to predict and is proven to be effective in differentiating between noisy and predictive variables in big data. It is introduced here to select influential variable modules or groups that account for compound interactions among explanatory variables. It is explainable regarding to what extent variables and their interactions contribute to the predictiveness of a classifier. In addition, I-score boosts the performance of classifiers over imbalanced datasets due to its association with the F1 score. Using predictive variables selected by I-score, interaction-based residual blocks are constructed over top I-score modules to generate predictors and ensemble learning aggregates these predictors to boost the prediction of the overall classifier. Experiments using naturalistic driving data show that our proposed classification method achieves the best accuracy (96%) for predicting MCI and dementia, followed by random forest (93%) and logistic regression (88%). In terms of F1 score and AUC, our proposed classifier achieves 98% and 87%, respectively, followed by random forest (with an F1 score of 96% and an AUC of 79%) and logistic regression (with an F1 score of 92% and an AUC of 77%). The results indicate that incorporating I-score into machine learning algorithms could considerably improve the model performance for predicting MCI and dementia in older drivers. We also performed the feature importance analysis and found that the right to left turn ratio and the number of hard braking events are the most important driving variables to predict MCI and dementia.

Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory

Being able to track and predict fluctuations in symptoms of mental health disorders such as bipolar disorder outside the clinic walls is critical for expanding access to care for the global population. To that end, we analyze a dataset of 291 individuals from a smartphone app targeted at bipolar disorder, which contains rich details about their smartphone interactions (including typing dynamics and accelerometer motion) collected everyday over several months, along with more traditional clinical features. The aim is to evaluate whether smartphone accelerometer data could serve as a proxy for traditional clinical data, either by itself or in combination with typing dynamics. Results show that accelerometer data improves the predictive performance of machine learning models by nearly 5% over those previously reported in the literature based only on clinical data and typing dynamics. This suggests it is possible to elicit essentially the same "information" about bipolar symptomology using different data sources, in a variety of settings.

Automatic classification of brain magnetic resonance images with hypercolumn deep features and machine learning

Brain tumours are life-threatening and their early detection is very important in a patient's life. At the present time, magnetic resonance imaging is one of the methods used for detecting brain tumours. Expert decision support systems serve specialist physicians to make more accurate diagnoses by minimizing the errors arising from their subjective opinions in real clinical settings. The model proposed in this study detects important keypoints and then extracts hypercolumn deep features of these keypoints from some convolutional layers of VGG16. Finally, Random Forest and Logistic Regression classifiers are fed with a set of these features. Random Forest classifier offered the best performance with 94.51% accuracy, 91.61% sensitivity, 8.39% false-negative rate, 97.42% specificity, and 97.29% precision using fivefold cross-validation in this study. Consequently, it is thought that the proposed model could contribute to field experts by integrating it into computer-aided brain magnetic resonance imaging diagnosis systems.

A modeling study of the effect of an alternating magnetic field on magnetite nanoparticles in proximity of the neuronal microtubules: A proposed mechanism for detachment of tau proteins

BACKGROUND AND OBJECTIVE

It is known that the disintegration of microtubules in neurons occurs in response to the phosphorylation of the tau proteins that promotes the structural instability of the microtubules, as one of the factors underlying the onset of Alzheimer's disease (AD).

METHODS

In this study, the mechanical variations undergone by the tau protein's and microtubule's structures due to the action of intrinsic magnetite nanoparticles inside the brain tissue have been computationally modeled using the finite element (FEM) method.

RESULTS

The von Mises stress induced by magnetite nanoparticles, subject to an applied alternating magnetic field, leads to local heating and mechanical forces, prompting a corresponding deformation in, and displacement of, the microtubule and the tau protein.

CONCLUSIONS

The induction of these deformations would increase the probability of the microtubules' depolymerization, and hence their eventual structural disintegration.

Early detection of Alzheimer's disease using single nucleotide polymorphisms analysis based on gradient boosting tree

Alzheimer's disease (AD) is a degenerative disorder that attacks nerve cells in the brain. AD leads to memory loss and cognitive & intellectual impairments that can influence social activities and decision-making. The most common type of human genetic variation is single nucleotide polymorphisms (SNPs). SNPs are beneficial markers of complex gene-disease. Many common and serious diseases, such as AD, have associated SNPs. Detection of SNP biomarkers linked with AD could help in the early prediction and diagnosis of this disease. The main objective of this paper is to predict and diagnose AD based on SNPs biomarkers with high classification accuracy in the early stages. One of the most concerning problems is the high number of features. Thus, the paper proposes a comprehensive framework for early AD detection and detecting the most significant genes based on SNPs analysis. Usage of machine learning (ML) techniques to identify new biomarkers of AD is also suggested. In the proposed system, two feature selection techniques are separately checked: the information gain filter and Boruta wrapper. The two feature selection techniques were used to select the most significant genes related to AD in this system. Filter methods measure the relevance of features by their correlation with dependent variables, while wrapper methods measure the usefulness of a subset of features by training a model on it. Gradient boosting tree (GBT) has been applied on all AD genetic data of neuroimaging initiative phase 1 (ADNI-1) and Whole-Genome Sequencing (WGS) datasets by using two feature selection techniques. In the whole-genome approach ADNI-1, results revealed that the GBT learning algorithm scored an overall accuracy of 99.06% in the case of using Boruta feature selection. Using information gain feature selection, the proposed system achieved an average accuracy of 94.87%. The results show that the proposed system is preferable for the early detection of AD. Also, the results revealed that the Boruta wrapper feature selection is superior to the information gain filter technique.

Machine Learning Framework for the Prediction of Alzheimer’s Disease Using Gene Expression Data Based on Efficient Gene Selection

In recent years, much research has focused on using machine learning (ML) for disease prediction based on gene expression (GE) data. However, many diseases have received considerable attention, whereas some, including Alzheimer’s disease (AD), have not, perhaps due to data shortage. The present work is intended to fill this gap by introducing a symmetric framework to predict AD from GE data, with the aim to produce the most accurate prediction using the smallest number of genes. The framework works in four stages after it receives a training dataset: pre-processing, gene selection (GS), classification, and AD prediction. The symmetry of the model is manifested in all of its stages. In the pre-processing stage gene columns in the training dataset are pre-processed identically. In the GS stage, the same user-defined filter metrics are invoked on every gene individually, and so are the same user-defined wrapper metrics. In the classification stage, a number of user-defined ML models are applied identically using the minimal set of genes selected in the preceding stage. The core of the proposed framework is a meticulous GS algorithm which we have designed to nominate eight subsets of the original set of genes provided in the training dataset. Exploring the eight subsets, the algorithm selects the best one to describe AD, and also the best ML model to predict the disease using this subset. For credible results, the framework calculates performance metrics using repeated stratified k-fold cross validation. To evaluate the framework, we used an AD dataset of 1157 cases and 39,280 genes, obtained by combining a number of smaller public datasets. The cases were split in two partitions, 1000 for training/testing, using 10-fold CV repeated 30 times, and 157 for validation. From the testing/training phase, the framework identified only 1058 genes to be the most relevant and the support vector machine (SVM) model to be the most accurate with these genes. In the final validation, we used the 157 cases that were never seen by the SVM classifier. For credible performance evaluation, we evaluated the classifier via six metrics, for which we obtained impressive values. Specifically, we obtained 0.97, 0.97, 0.98, 0.945, 0.972, and 0.975 for the sensitivity (recall), specificity, precision, kappa index, AUC, and accuracy, respectively.

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

BACKGROUND AND OBJECTIVES

This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges.

METHODS

We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail.

RESULTS

It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes.

CONCLUSIONS

Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images

Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machine learning approaches built with different classifiers in the detection of this disease. This paper proposes an approach based on deep learning which detects Covid-19 and no-finding cases using chest X-ray images. Here, the classification performance of the Bi-LSTM network on the deep features was compared with the Deep Neural Network within the frame of the fivefold cross-validation technique. Accuracy, sensitivity, specificity and precision metrics were used to evaluate the classification performance of the trained models. Bi-LSTM network presented better performance compare to DNN with 97.6% value of high accuracy despite the few numbers of Covid-19 images in the dataset. In addition, it is understood that concatenated deep features more meaningful than deep features obtained with pre-trained networks by one by, as well. Consequently, it is thought that the proposed study based on the Bi-LSTM network and concatenated deep features will be noteworthy in the design of highly sensitive automated Covid-19 monitoring systems.

Convolutional neural networks for Alzheimer's disease detection on MRI images

Purpose: Detection of Alzheimer's disease (AD) on magnetic resonance imaging (MRI) using convolutional neural networks (CNNs), which is useful for detecting AD in its preliminary states. Approach: Our study implements and compares several deep models and configurations, including two-dimensional (2D) and three-dimensional (3D) CNNs and recurrent neural networks (RNNs). To use a 2D CNN on 3D MRI volumes, each MRI scan is split into 2D slices, neglecting the connection among 2D image slices in an MRI volume. Instead, a CNN model could be followed by an RNN in a way that the model of 2D CNN + RNN can understand the connection among sequences of 2D image slices for an MRI. The issue is that the feature extraction step in the 2D CNN is independent of classification in the RNN. To tackle this, 3D CNNs can be employed instead of 2D CNNs to make voxel-based decisions. Our study's main contribution is to introduce transfer learning from a dataset of 2D images to 3D CNNs. Results: The results on our MRI dataset indicate that sequence-based decisions improve the accuracy of slice-based decisions by 2% in classifying AD patients from healthy subjects. Also the 3D voxel-based method with transfer learning outperforms the other methods with 96.88% accuracy, 100% sensitivity, and 94.12% specificity. Conclusions: Several implementations and experiments using CNNs on MRI scans for AD detection demonstrated that the voxel-based method with transfer learning from ImageNet to MRI datasets using 3D CNNs considerably improved the results compared with the others.