Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging

Chen, Yuanyuan; Huang, Zhaowei; Xia, Yong

doi:10.1007/978-3-030-00563-4_29

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…Thus, MCI sufferers are then categorized as MCI converters and MCI nonconverters. However, the exact causes of AD are yet not well understood to healthcare professionals and no proven medicines or therapies have even been confirmed to prevent or reverse the progression of AD [ 23 ].…”

Section: Introductionmentioning

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

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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.

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Section: Introductionmentioning

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

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“…The use of CNN for effective classification of MRI scans is similar to the more ordinary neural networks in that they are made up of hidden layers consisting of neurons with learnable parameters [ 23 ]. However, the earlier proposed methodologies by the researchers clearly lags automatically learning the hierarchical feature representation of images which otherwise can be utilized based on the deep structure for effective binary as well as multiclass classification [ 28 , 44 , 61 ]. Table 4 outlays a state-of-the-art comparison of diverse datasets and modeling methodologies, allowing for a relevant assessment of DL, transfer learning, and hybrid learning effectiveness.…”

Section: Methodology and Implementationmentioning

confidence: 99%

“…As a result, MCI participants usually split into 2 groups: convertible MCI and nonconvertible MCI [ 27 ]. Unfortunately, the underlying etiology of AD is still obscure to healthcare experts, and also no recognized treatments or remedies have been shown to avoid or reverse the development of AD [ 28 ]. Some of the ML and DL(CAD) based AD classification techniques will be discussed further below.…”

Section: Related Workmentioning

confidence: 99%

A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices

Sethi

Rani

Singh

et al. 2022

Computational and Mathematical Methods in Medicine

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Developments in medical care have inspired wide interest in the current decade, especially to their services to individuals living prolonged and healthier lives. Alzheimer’s disease (AD) is the most chronic neurodegeneration and dementia-causing disorder. Economic expense of treating AD patients is expected to grow. The requirement of developing a computer-aided technique for early AD categorization becomes even more essential. Deep learning (DL) models offer numerous benefits against machine learning tools. Several latest experiments that exploited brain magnetic resonance imaging (MRI) scans and convolutional neural networks (CNN) for AD classification showed promising conclusions. CNN’s receptive field aids in the extraction of main recognizable features from these MRI scans. In order to increase classification accuracy, a new adaptive model based on CNN and support vector machines (SVM) is presented in the research, combining both the CNN’s capabilities in feature extraction and SVM in classification. The objective of this research is to build a hybrid CNN-SVM model for classifying AD using the MRI ADNI dataset. Experimental results reveal that the hybrid CNN-SVM model outperforms the CNN model alone, with relative improvements of 3.4%, 1.09%, 0.85%, and 2.82% on the testing dataset for AD vs. cognitive normal (CN), CN vs. mild cognitive impairment (MCI), AD vs. MCI, and CN vs. MCI vs. AD, respectively. Finally, the proposed approach has been further experimented on OASIS dataset leading to accuracy of 86.2%.

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“…In the final stages of AD, dementia symptoms gradually deteriorate over many years. Therefore, early identification (Chen et al, 2018;Lu et al, 2018) is essential to prevent worsening health conditions. A probable diagnosis is based on illness and cognitive testing history with medical imaging and blood test to rule out other possible causes.…”

Section: Introductionmentioning

confidence: 99%

Whale Optimized Deep Generative Adversarial Network Based Alzheimer's Stages Detection Using 3D MRI Brain Neuroimaging

Sampath,

Baskar

2023

Journal of Computer Science

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Alzheimer's Disease (AD), a common, chronic neurodegenerative condition, is characterized by the loss of neurons and synapses in the cerebral cortex and specific subcortical regions. According to claims from a recent study, AD has a 20% misdiagnosis rate. Therefore, it is essential to create a useful tool to recognize the stages of AD with a lower prediction error rate to reduce misdiagnosis. Hence proposed a model called Whale-Optimized Deep Generative Adversarial Network (WODGAN). A generator plus a discriminator make up the model. The discriminator trains the model using real images; The generator creates synthetic images using noise and random selection. The discriminator goes through some processes to improve image quality, including Adaptive Histogram Equalization (AHE) and Adaptive Filtering (AF) approaches. Fuzzy feature extraction techniques are used to accurately segregate biomarker regions from brain MRI scans depending on AD pathology. The model uses Hilbert-Schmidt Independence Criterion Lasso (HSICL) to discover optimized biomarker features to combat overfitting. Before training, the discriminator can tell actual photos from artificial ones. The Whale Optimizer (WO) is used during training to improve network efficiency and lower prediction errors. The numerical results show a high accuracy of 99.93% in AD stage recognition.

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Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging

Cited by 6 publications

References 26 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

A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices

Whale Optimized Deep Generative Adversarial Network Based Alzheimer's Stages Detection Using 3D MRI Brain Neuroimaging

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