Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images DOI Creative Commons
Samy E. Oraby, Ahmed A. Emran, Basel Mounir El Saghir

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 13, 2025

In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with convolutional neural (CNN) model designed to classify four stages of Alzheimer's disease (AD): Mild Dementia (MD), Moderate (MOD), Non-Demented (ND), and Very (VMD). The proposed DSR-GAN is implemented using PyTorch library uses dataset 6,400 MRI images. A (SR) technique applied enhance the clarity detail images, allowing refine particular image features. CNN undergoes hyperparameter optimization incorporates data augmentation strategies maximize its efficiency. normalized error matrix area under ROC curve are used experimentally evaluate CNN's performance which achieved testing accuracy 99.22%, an 100%, rate 0.0516. Also, assessed three different metrics: structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), multi-scale (MS-SSIM). SSIM score 0.847, while PSNR MS-SSIM percentage 29.30 dB 96.39%, respectively. combination models provides rapid precise method distinguish between various disease, potentially aiding professionals in screening AD cases.

Language: Английский

Application of Transfer Learning for Biomedical Signals: A Comprehensive Review of the Last Decade (2014-2024) DOI Creative Commons
Mahboobeh Jafari, Xiaohui Tao, Prabal Datta Barua

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: 118, P. 102982 - 102982

Published: Jan. 30, 2025

Language: Английский

Citations

1
Boruta Feature Selection and Deep Learning for Alzheimer’s Disease Classification DOI Creative Commons

Ramu S. Siddaganga,

Nagaraj Naik,

H A Dinesha

et al.

International Journal of Statistics in Medical Research, Journal Year: 2025, Volume and Issue: 14, P. 145 - 152

Published: March 25, 2025

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and functional deterioration. The early accurate classification of AD crucial for timely intervention management. This study utilizes the Boruta feature selection method to identify most relevant features classification, selecting top 15 based on importance ranking. Three machine learning models—Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), Support Vector Machines (SVM)—were evaluated using accuracy, precision, recall, F1-score as performance metrics. LSTM model demonstrated highest accuracy (89.30%), outperforming DNN (88.14%) SVM (84.19%), owing its capability capturing temporal dependencies in inpatient data. Results indicate that deep models offer superior compared traditional approaches classification. emphasizes cognitive, lifestyle, metabolic diagnosis while acknowledging limitations such dataset constraints interpretability. Future research should improve explainability, incorporate multi-modal data, leverage real-time monitoring techniques enhanced detection.

Language: Английский

Citations

0
Harnessing Explainable Artificial Intelligence (XAI) based SHAPLEY Values and Ensemble Techniques for Accurate Alzheimer's Disease Diagnosis DOI Open Access

R. Balakrishnan,

Manyam Rajasekhar Reddy,

Prasad Theeda

et al.

Engineering Technology & Applied Science Research, Journal Year: 2025, Volume and Issue: 15(2), P. 20743 - 20747

Published: April 3, 2025

Machine Learning (ML) is a dynamic method for managing extensive datasets to uncover significant patterns and hidden insights. ML has revolutionized numerous industries, from healthcare finance, entertainment transportation. Ensemble classifiers combined with Explainable AI (XAI) have surfaced as asset in the field of Alzheimer's Disease (AD) diagnosis. Boosting EC techniques coupled Shapley Additive Explanations (SHAP) offers powerful approach AD This paper investigates boosting ensemble schemes, such XGBoost, LightGBM, Gradient (GB), diagnosis SHAP feature selection. The proposed scheme achieved efficient results, an accuracy more than 94% minimum features detection process.

Language: Английский

Citations

0
Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images DOI Creative Commons
Samy E. Oraby, Ahmed A. Emran, Basel Mounir El Saghir

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 13, 2025

In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with convolutional neural (CNN) model designed to classify four stages of Alzheimer's disease (AD): Mild Dementia (MD), Moderate (MOD), Non-Demented (ND), and Very (VMD). The proposed DSR-GAN is implemented using PyTorch library uses dataset 6,400 MRI images. A (SR) technique applied enhance the clarity detail images, allowing refine particular image features. CNN undergoes hyperparameter optimization incorporates data augmentation strategies maximize its efficiency. normalized error matrix area under ROC curve are used experimentally evaluate CNN's performance which achieved testing accuracy 99.22%, an 100%, rate 0.0516. Also, assessed three different metrics: structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), multi-scale (MS-SSIM). SSIM score 0.847, while PSNR MS-SSIM percentage 29.30 dB 96.39%, respectively. combination models provides rapid precise method distinguish between various disease, potentially aiding professionals in screening AD cases.

Language: Английский

Citations

0