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

Venugopalan, Janani; Li, Tong; Hassanzadeh, Hamid; Wang, May D.

doi:10.1038/s41598-020-74399-w

Cited by 355 publications

(183 citation statements)

References 45 publications

(22 reference statements)

Supporting

Mentioning

145

Contrasting

Order By: Relevance

“…Their model achieved a good performance level. Researchers in [55] used the DL model to integrally analyse MRI, genetic, and clinical datasets to categorize subjects into CN, MCI, and AD. Specifically, autoncoders stacked for genetic and clinical data extraction and the 3D-CNN for MRI neuroimaging data have been employed.…”

Section: Related Workmentioning

confidence: 99%

Classification of Alzheimer’s Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network

Sethi

Ahuja

Rani

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.

show abstract

Section: Related Workmentioning

confidence: 99%

Classification of Alzheimer’s Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network

Sethi

Ahuja

Rani

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Venugopalan et al conducted a study to determine the stage of Alzheimer's disease. They emphasized the success of deep learning methods by comparing deep learning methods with other machine learning methods in identifying various regions of the brain on brain MRI images [44]. Atila et al conducted a study that used deep learning architecture to classify diseases observed in the leaves of plants [45].…”

Section: Related Workmentioning

confidence: 99%

Diagnosing Hematological Disorders Using Deep Learning Method

Karagül

Yurtay

Öneç

2021

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

Deciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.

show abstract

“…The authors in El-Sappagh et al [ 18 ] used ensemble machine learning classifiers based on RF for the two layers, utilizing multimodal AD datasets. Venugopalan et al [ 19 ] used different models including, SVM, DT, RF, and KNN, to early detect the AD stage. In addition, they demonstrated multimodality data and single-modality models.…”

Section: Introductionmentioning

confidence: 99%

The Role of Medication Data to Enhance the Prediction of Alzheimer’s Progression Using Machine Learning

El–Sappagh

Abuhmed

Alouffi

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Early detection of Alzheimer’s disease (AD) progression is crucial for proper disease management. Most studies concentrate on neuroimaging data analysis of baseline visits only. They ignore the fact that AD is a chronic disease and patient’s data are naturally longitudinal. In addition, there are no studies that examine the effect of dementia medicines on the behavior of the disease. In this paper, we propose a machine learning-based architecture for early progression detection of AD based on multimodal data of AD drugs and cognitive scores data. We compare the performance of five popular machine learning techniques including support vector machine, random forest, logistic regression, decision tree, and K-nearest neighbor to predict AD progression after 2.5 years. Extensive experiments are performed using an ADNI dataset of 1036 subjects. The cross-validation performance of most algorithms has been improved by fusing the drugs and cognitive scores data. The results indicate the important role of patient’s taken drugs on the progression of AD disease.

show abstract

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

Cited by 355 publications

References 45 publications

Classification of Alzheimer’s Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network

Classification of Alzheimer’s Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network

Diagnosing Hematological Disorders Using Deep Learning Method

The Role of Medication Data to Enhance the Prediction of Alzheimer’s Progression Using Machine Learning

Contact Info

Product

Resources

About