Deep-Learning-Based Diagnosis and Prognosis of Alzheimer’s Disease: A Comprehensive Review

Sharma, Rahul; Goel, Tripti; Tanveer, M.; Lin, Chin-Teng; Murugan, R.

doi:10.1109/tcds.2023.3254209

Cited by 17 publications

(1 citation statement)

References 105 publications

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“…According to [32], a deep learning system can improve brain MRI-based Alzheimer's disease prognosis, with some studies reaching an accuracy of 90%. More studies are needed to confirm these results in different populations [33]. The summary of the detailed model is discussed in the Related Work section with the dataset being used and accuracy presented in Table 1.…”

Section: Related Workmentioning

confidence: 99%

Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization

Mohammad,

Al Ahmadi

2023

Mathematics

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Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder that affects a substantial proportion of the population. The accurate and timely prediction of AD carries considerable importance in enhancing the diagnostic process and improved treatment. This study provides a thorough examination of AD prediction using the VGG19 deep learning model. The primary objective of this study is to investigate the effectiveness of feature fusion and optimization techniques in enhancing the accuracy of classification. The generation of a comprehensive feature map is achieved through the fusion of features that have been extracted from the fc7 and fc8 layers of VGG19. Several machine learning algorithms are employed to classify integrated features and recognize AD. The amalgamated feature map demonstrates a significant level of accuracy of 98% in the prognostication of AD, outperforming present cutting-edge methodologies. In this study, a methodology is utilized that makes use of the whale optimization algorithm (WoA), a metaheuristic approach to optimize features through feature selection. Feature optimization aims to eliminate redundant features and enhance the discriminatory power of the selected features. Following the optimization procedure, the F-KNN algorithm attained a precision level of 99%, surpassing the present state-of-the-art (SOTA) results reported in the current literature.

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Section: Related Workmentioning

confidence: 99%

Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization

Mohammad,

Al Ahmadi

2023

Mathematics

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Decoding cognitive health using machine learning: A comprehensive evaluation for diagnosis of significant memory concern

Sajid,

Sharma,

Beheshti

et al. 2024

WIREs Data Min & Knowl

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The timely identification of significant memory concern (SMC) is crucial for proactive cognitive health management, especially in an aging population. Detecting SMC early enables timely intervention and personalized care, potentially slowing cognitive disorder progression. This study presents a state‐of‐the‐art review followed by a comprehensive evaluation of machine learning models within the randomized neural networks (RNNs) and hyperplane‐based classifiers (HbCs) family to investigate SMC diagnosis thoroughly. Utilizing the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset, 111 individuals with SMC and 111 healthy older adults are analyzed based on T1W magnetic resonance imaging (MRI) scans, extracting rich features. This analysis is based on baseline structural MRI (sMRI) scans, extracting rich features from gray matter (GM), white matter (WM), Jacobian determinant (JD), and cortical thickness (CT) measurements. In RNNs, deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) emerge as the best classifiers in terms of performance metrics in the identification of SMC. In HbCs, Kernelized pinball general twin support vector machine (Pin‐GTSVM‐K) excels in CT and WM features, whereas Linear Pin‐GTSVM (Pin‐GTSVM‐L) and Linear intuitionistic fuzzy TSVM (IFTSVM‐L) performs well in the JD and GM features sets, respectively. This comprehensive evaluation emphasizes the critical role of feature selection, feature based‐interpretability and model choice in attaining an effective classifier for SMC diagnosis. The inclusion of statistical analyses further reinforces the credibility of the results, affirming the rigor of this analysis. The performance measures exhibit the suitability of this framework in aiding researchers with the automated and accurate assessment of SMC. The source codes of the algorithms and datasets used in this study are available at https://github.com/mtanveer1/SMC.This article is categorized under: Technologies > Classification Technologies > Machine Learning Application Areas > Health Care

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