Deep Learning Model to Evaluate Alzheimer's disease Through Multi-View Clustering DOI Open Access

Sneha Nimbare,

Priyanka Paygude, Amol Dhumane

et al.

International Research Journal of Multidisciplinary Technovation, Journal Year: 2024, Volume and Issue: unknown, P. 33 - 46

Published: Dec. 30, 2024

Early diagnosis of Alzheimer's disease (AD) plays a crucial role in the development and effectiveness interventions, neuroimaging stands out as an up-and-coming field for initial identification disease. Earlier models utilized various methods to analyze images disease, such deep learning or unsupervised matrix factorization processes. Neither these techniques alone can produce satisfactory results while clustering multi-view photos This motivates our research create model obtaining most important factors from MRI classifying brain into different stages. To achieve optimal clustering, proposed integrates technique (Channel Boost-Convolution Neural Network) with inverse method, forming ensemble approach. The experiment analyzes several evaluate implemented performance RMSE, which are about 2.32 better than compared models. show that combining Inverse image works well, Transformers further improve learning.

Language: Английский

Emerging Trends in Deep Learning for Early Alzheimer's Disease Diagnosis and Classification: A Comprehensive Review DOI Open Access

S. Gokul Amuthan,

Naveen Kumar

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 4, 2025

Alzheimer's Disease (AD), a progressive neurodegenerative disorder, manifests as cognitive decline and memory loss, significantly impacting individuals' lives healthcare systems globally. Early diagnosis intervention are crucial for improving patient outcomes managing the disease effectively. Recent advancements in deep learning (DL) have shown substantial promise medical image classification early AD diagnosis. This survey evaluates state-of-the-art DL techniques, including hybrid models, Recurrent Neural Networks (RNNs), Convolutional (CNNs), applied across imaging modalities such computed tomography (CT), positron emission (PET), magnetic resonance (MRI). It emphasizes their performance, accuracy, computational efficiency while addressing critical challenges like need large annotated datasets, overfitting, model interpretability. Furthermore, explores how could revolutionize identifies future research directions to bridge existing gaps, aiming improve detection personalized diagnostic approaches individuals with AD.

Language: Английский

Citations

9
Exploring the role of Convolutional Neural Networks (CNN) in dental radiography segmentation: A comprehensive Systematic Literature Review DOI
Walid Brahmi, Imen Jdey, Fadoua Drira

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108510 - 108510

Published: May 11, 2024

Language: Английский

Citations

5
Advanced Implementation of Convolutional Neural Networks for Alzheimer's Diseases Diagnosis DOI
Vivek Gondalia, Kalpesh Popat

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(4)

Published: March 28, 2025

Language: Английский

Citations

0
Enhancing multi-class neurodegenerative disease classification using deep learning and explainable local interpretable model-agnostic explanations DOI Creative Commons
Jamel Baili, Abdullah Alqahtani, Ahmad Almadhor

et al.

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: April 1, 2025

Alzheimer's disease (AD) and Parkinson's (PD) are two of the most prevalent neurodegenerative disorders, necessitating accurate diagnostic approaches for early detection effective management. This study introduces deep learning architectures, Residual-based Attention Convolutional Neural Network (RbACNN) Inverted (IRbACNN), designed to enhance medical image classification AD PD diagnosis. By integrating self-attention mechanisms, these models improve feature extraction, interpretability, address limitations traditional methods. Additionally, explainable AI (XAI) techniques incorporated provide model transparency clinical trust in automated diagnoses. Preprocessing steps such as histogram equalization batch creation applied optimize quality balance dataset. The proposed achieved an outstanding accuracy 99.92%. results demonstrate that combination with XAI, facilitate precise diagnosis, thereby contributing reducing global burden diseases.

Language: Английский

Citations

0
From Handwriting Analysis to Alzheimer’s Disease Prediction: An Experimental Comparison of Classifier Combination Methods DOI
Tiziana D’Alessandro, Claudio De Stefano, Francesco Fontanella

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 334 - 351

Published: Jan. 1, 2024

Language: Английский

Citations

2
Alzheimer’s Disease and Mild Cognitive Impairment Detection Using sMRI With Efficient Receptive Field and Enhanced Multi-Axis Attention Fusion DOI Creative Commons
Uttam Khatri, Jun‐Hyung Kim, Goo‐Rak Kwon

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 100848 - 100861

Published: Jan. 1, 2024

Deep neural networks have shown promising results in the analysis of structural magnetic resonance imaging (sMRI) data for diagnosis dementia, particularly Alzheimer's disease (AD). Different regions brain diverse structures that are linked to specific functions, which could account variability disease-related changes observed sMRI scans these areas. Understanding overall characteristics is important since current popular convolutional (CNN) deep learning do not consider interconnection voxels. Vision transformers effectiveness identifying long-distance connections brain, has led their success applications such as detection. However, image noise and limited scalability self-attention mechanisms relation size hindered widespread use advanced analysis. To enhance information retention reduce network complexity, this study presents a novel adaptable efficient receptive field feature extraction network. Moreover, an attention mechanism with both local grid block dilated global module been incorporated highlight AD. Next, more improved hierarchical inverted residual feed forward place multi-layer perceptron suggested characterization features through integration from lower higher layers. Finally, average pooling $1\times 1$ convolution used dimensionality, non-linearity, allow channel interactions maps before being input into classification head. The achieved high performance various scenarios, accuracies 97.29% AD vs. HC 94.79% MCI using ADNI experimental datasets.

Language: Английский

Citations

0
Deep Learning Model to Evaluate Alzheimer's disease Through Multi-View Clustering DOI Open Access

Sneha Nimbare,

Priyanka Paygude, Amol Dhumane

et al.

International Research Journal of Multidisciplinary Technovation, Journal Year: 2024, Volume and Issue: unknown, P. 33 - 46

Published: Dec. 30, 2024

Early diagnosis of Alzheimer's disease (AD) plays a crucial role in the development and effectiveness interventions, neuroimaging stands out as an up-and-coming field for initial identification disease. Earlier models utilized various methods to analyze images disease, such deep learning or unsupervised matrix factorization processes. Neither these techniques alone can produce satisfactory results while clustering multi-view photos This motivates our research create model obtaining most important factors from MRI classifying brain into different stages. To achieve optimal clustering, proposed integrates technique (Channel Boost-Convolution Neural Network) with inverse method, forming ensemble approach. The experiment analyzes several evaluate implemented performance RMSE, which are about 2.32 better than compared models. show that combining Inverse image works well, Transformers further improve learning.

Language: Английский

Citations

0