AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images

Shamrat, F. M. Javed Mehedi; Akter, Shamima; Azam, Sami; Karim, Asif; Ghosh, Pronab; Tasnim, Zarrin; Hasib, Khan Md.; Boer, Friso De; Ahmed, Kawsar

doi:10.1109/access.2023.3244952

Cited by 77 publications

(23 citation statements)

References 54 publications

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“…The authors of [48] employed DL approaches to determine AD using the disease ontology method and accurately predicted AD with an accuracy of 94.61%. Shamrat et al [28] proposed an Alzheimer Net to predict AD robustly using DL approaches, with an accuracy of 98.67%. The proposed DL-based method demonstrated superior performance compared with the state-of-the-art (SOTA) methodologies presented in Table 9.…”

Section: Ad Prediction Results Of Feature Optimizationmentioning

confidence: 99%

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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: Ad Prediction Results Of Feature Optimizationmentioning

confidence: 99%

“…Using MRI images, the authors of [28] classified Alzheimer's disease using deep learning algorithms. Compared with conventional machine learning approaches, the accuracy of AD prediction using DL algorithms was much higher.…”

Section: Related Workmentioning

confidence: 99%

Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization

Mohammad,

Al Ahmadi

2023

Mathematics

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“…In this manuscript, the proposed HBOA-MLP system's performance is tested on a benchmark dataset named ADNI [31]. The ADNI is a multi-site study, which aims in gen-…”

Section: Database Description and Denoisingmentioning

confidence: 99%

fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

Chelladurai,

Narayan,

Divakarachari

et al. 2023

Brain Sciences

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In the present scenario, Alzheimer’s Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.

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“…AD is such a serious brain disease that it can result in a patient's death if not effectively treated (Gómez-Isla and Frosch, 2022 ). To overcome this disease, patients need good care, regular exercise, and some memory-sharpening activities as there is currently no specific medication for AD (Shamrat et al, 2023 ). In recent years, a significant increase has been observed in AD (Mirzaei and Adeli, 2022 ; Stevenson-Hoare et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning (DL) (Shaukat et al, 2022 ) is a subtype of machine learning that falls under the umbrella of artificial intelligence, but DL is way more vigorous and flexible in comparison with ML (Fabrizio et al, 2021 ). Techniques such as shallow CNN (Marwa et al, 2023 ), DNN (Hazarika et al, 2023 ), MultiAz-Net (Ismail et al, 2023 ), hybridized DL method (Hashmi, 2024 ), and RVFL (Goel et al, 2023 ) have been used in recent years, but these techniques yield low accuracy as compared to our proposed model (Shamrat et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection

Yaqoob,

Khan,

Masood

et al. 2024

Front. Comput. Neurosci.

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Alzheimer's disease (AD) is a neurodegenerative illness that impairs cognition, function, and behavior by causing irreversible damage to multiple brain areas, including the hippocampus. The suffering of the patients and their family members will be lessened with an early diagnosis of AD. The automatic diagnosis technique is widely required due to the shortage of medical experts and eases the burden of medical staff. The automatic artificial intelligence (AI)-based computerized method can help experts achieve better diagnosis accuracy and precision rates. This study proposes a new automated framework for AD stage prediction based on the ResNet-Self architecture and Fuzzy Entropy-controlled Path-Finding Algorithm (FEcPFA). A data augmentation technique has been utilized to resolve the dataset imbalance issue. In the next step, we proposed a new deep-learning model based on the self-attention module. A ResNet-50 architecture is modified and connected with a self-attention block for important information extraction. The hyperparameters were optimized using Bayesian optimization (BO) and then utilized to train the model, which was subsequently employed for feature extraction. The self-attention extracted features were optimized using the proposed FEcPFA. The best features were selected using FEcPFA and passed to the machine learning classifiers for the final classification. The experimental process utilized a publicly available MRI dataset and achieved an improved accuracy of 99.9%. The results were compared with state-of-the-art (SOTA) techniques, demonstrating the improvement of the proposed framework in terms of accuracy and time efficiency.

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AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images

Cited by 77 publications

References 54 publications

Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization

Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization

fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection

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