An Exploration: Alzheimer’s Disease Classification Based on Convolutional Neural Network

Sethi, Monika; Ahuja, Sachin; Rani, Shalli; Koundal, Deepika; Zaguia, Atef; Enbeyle, Wegayehu

doi:10.1155/2022/8739960

Cited by 40 publications

(21 citation statements)

References 68 publications

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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%

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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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Section: Methodology and Implementationmentioning

confidence: 99%

“…Convolutional Neural Network. During the last decade, CNN has achieved ground breaking findings in a wide range of domains such as pattern recognition domains, from computer vision to speech classification [44,45]. One of most advantageous element of CNNs is that they result in fewer of parameters in ANN.…”

Section: Theoretical Backgroundmentioning

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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“…The proposed CNN was employed on three-dimensional T1-weighted images to distinguish AD and mild cognitive disorders, and they reported 98% accuracy. Although CNNs have achieved significant success in identifying AD, there are several challenges arising from the limited availability of medical data and their potential application in such domains [ 29 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Alzheimer’s Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA

Ahmad,

Siddiqi,

Alhujaili

et al. 2023

Healthcare

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The examination of Alzheimer’s disease (AD) using adaptive machine learning algorithms has unveiled promising findings. However, achieving substantial credibility in medical contexts necessitates a combination of notable accuracy, minimal processing time, and universality across diverse populations. Therefore, we have formulated a hybrid methodology in this study to classify AD by employing a brain MRI image dataset. We incorporated an averaging filter during preprocessing in the initial stage to reduce extraneous details. Subsequently, a combined strategy was utilized, involving principal component analysis (PCA) in conjunction with stepwise linear discriminant analysis (SWLDA), followed by an artificial neural network (ANN). SWLDA employs a combination of forward and backward recursion methods to choose a restricted set of features. The forward recursion identifies the most interconnected features based on partial Z-test values. Conversely, the backward recursion method eliminates the least correlated features from the same feature space. After the extraction and selection of features, an optimized artificial neural network (ANN) was utilized to differentiate the various classes of AD. To demonstrate the significance of this hybrid approach, we utilized publicly available brain MRI datasets using a 10-fold cross-validation strategy. The proposed method excelled over existing state-of-the-art systems, attaining weighted average recognition rates of 99.35% and 96.66%, respectively, across all the datasets.

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“…Authors in [9] investigated the effectiveness of different DLbased classification algorithms for the task of AD classification Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content.…”

Section: Literature Reviewmentioning

confidence: 99%

Quantifying Uncertainty in Automated Detection of Alzheimer’s Patients Using Deep Neural Network

Roshanzamir¹,

Shamsi²,

Asgharnezhad³

et al. 2023

Preprint

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One of the most common forms of dementia is Alzheimer’s disease (AD), which leads to progressive mental deterioration. Unfortunately, there is no definitive diagnosis and cure that can stop the condition progressing. The diagnosis is often performed based on the clinical history and neuropsychological data, including magnetic resonance imaging (MRI). Deep neural networks (DNN) algorithms are gaining popularity for medical diagnosis, and have been used widely for the analysis of MRI data. DNNs can extract hidden features from thousands of training images automatically. However, they cannot judge how confident they are about their predictions. To use DNNs in safety-critical applications such as medical diagnosis, uncertainty quantification of DNNs predictions is crucial. For this purpose, Monte Carlo dropout (MCD) has been widely used, however, it may lead to overconfident and miss calibrated results. This paper proposes a framework in which the MCD algorithm’s hyper-parameters are optimized during training using Bayesian optimization for the first time. The conducted optimization leads to assigning high predictive entropy to erroneous predictions and making it possible to recognize risky predictions. The proposed framework is used for AD diagnosis, which has not been done before. We compare our method with some existing methods in the literature based on different uncertainty quantification criteria. The results of comprehensive experiments on the Kaggle dataset using a deep model pre-trained on the ImageNet dataset show that the proposed algorithm can quantify uncertainty much better than the existing methods.

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An Exploration: Alzheimer’s Disease Classification Based on Convolutional Neural Network

Cited by 40 publications

References 68 publications

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

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

Improving Alzheimer’s Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA

Quantifying Uncertainty in Automated Detection of Alzheimer’s Patients Using Deep Neural Network

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