Ghadah Naif Alwakid^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068666 - 10 November 2025

Abstract Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that significantly affects cognitive function, making early and accurate diagnosis essential. Traditional Deep Learning (DL)-based approaches often struggle with low-contrast MRI images, class imbalance, and suboptimal feature extraction. This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans. Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient (MCC)-based evaluation method into the design.… More >

Huihui Yang¹, Saif Ur Rehman Khan^2,*, Omair Bilal², Chao Chen^1,*, Ming Zhao²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2401-2434, 2025, DOI:10.32604/cmes.2025.072148 - 26 November 2025

Abstract Detecting Alzheimer’s disease is essential for patient care, as an accurate diagnosis influences treatment options. Classifying dementia from non-dementia in brain MRIs is challenging due to features such as hippocampal atrophy, while manual diagnosis is susceptible to error. Optimal computer-aided diagnosis (CAD) systems are essential for improving accuracy and reducing misclassification risks. This study proposes an optimized ensemble method (CEOE-Net) that initiates with the selection of pre-trained models, including DenseNet121, ResNet50V2, and ResNet152V2 for unique feature extraction. Each selected model is enhanced with the inclusion of a channel attention (CA) block to improve the feature… More >

Abdullah G. M. Almansour^1,*, Faisal Alshomrani², Abdulaziz T. M. Almutairi³, Easa Alalwany⁴, Mohammed S. Alshuhri¹, Hussein Alshaari⁵, Abdullah Alfahaid⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2395-2418, 2025, DOI:10.32604/cmes.2025.069661 - 31 August 2025

Abstract The early and precise identification of Alzheimer’s Disease (AD) continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases. This study presents a novel Deformable Attention Vision Transformer (DA-ViT) architecture that integrates deformable Multi-Head Self-Attention (MHSA) with a Multi-Layer Perceptron (MLP) block for efficient classification of Alzheimer’s disease (AD) using Magnetic resonance imaging (MRI) scans. In contrast to traditional vision transformers, our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions, markedly diminishing processing demands while improving localized feature representation. DA-ViT contains only More >

Carmen Rubio^1,#, Norma Serrano-GarcíA^1,#, Elisa Taddei¹, Eduardo CastañEda², HéCtor Romo^1,3, MoiséS Rubio-Osornio^4,*

BIOCELL, Vol.49, No.8, pp. 1391-1412, 2025, DOI:10.32604/biocell.2025.066320 - 29 August 2025

Abstract Rotenone is a lipophilic herbicide extensively utilized in experimental neurodegenerative models because of its capacity to disrupt complex I of the mitochondrial electron transport chain. This inhibition results in reduced ATP synthesis, elevated reactive oxygen species (ROS) formation, and mitochondrial malfunction, which instigates oxidative stress and cellular damage, critical elements in neurodegenerative disorders like Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and Alzheimer’s disease (AD). In addition to causing direct neuronal injury, rotenone significantly contributes to the activation of glial cells, specifically microglia and astrocytes. Activated microglia assumes a proinflammatory (M1) phenotype, distinguished by the… More >

Nadhmi A. Gazem¹, Sultan Noman Qasem^2,3, Umair Naeem⁴, Shahid Latif⁵, Ibtehal Nafea⁶, Faisal Saeed⁷, Mujeeb Ur Rehman^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 407-427, 2025, DOI:10.32604/cmes.2025.066517 - 31 July 2025

Abstract Falls are a leading cause of injury and morbidity among older adults, especially those with Alzheimer’s disease (AD), who face increased risks due to cognitive decline, gait instability, and impaired spatial awareness. While wearable sensor-based fall detection systems offer promising solutions, their effectiveness is often hindered by domain shifts resulting from variations in sensor placement, sampling frequencies, and discrepancies in dataset distributions. To address these challenges, this paper proposes a novel unsupervised domain adaptation (UDA) framework specifically designed for cross-dataset fall detection in Alzheimer’s disease (AD) patients, utilizing advanced transfer learning to enhance generalizability. The… More >

Taiyang Zhu¹, Fang Hua^2,*

BIOCELL, Vol.49, No.7, pp. 1169-1184, 2025, DOI:10.32604/biocell.2025.064879 - 25 July 2025

Abstract Alzheimer’s disease (AD) is a neurological disorder characterized primarily by a progressive decline in cognitive and behavioral functions. The pathogenesis of AD has not been fully elucidated till now. The progesterone receptor (PR) family has recently attracted increasing attention and has become the focus of potential links to factors such as the pathogenesis and pathological changes of AD due to its role in the central nervous system. This article summarizes the progress of research progress on the PR family in AD, including its role in pathophysiology, molecular mechanisms, and potential therapeutic strategies. More >

Walaa N. Ismail^1,2,#,*, Fathimathul Rajeena P. P.^3,#, Mona A. S. Ali^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3709-3741, 2025, DOI:10.32604/cmes.2025.065564 - 30 June 2025

Abstract Early detection of Alzheimer’s disease (AD) is crucial, particularly in resource-constrained medical settings. This study introduces an optimized deep learning framework that conceptualizes neural networks as computational “sensors” for neurodegenerative diagnosis, incorporating feature selection, selective layer unfreezing, pruning, and algorithmic optimization. An enhanced lightweight hybrid DenseNet201 model is proposed, integrating layer pruning strategies for feature selection and bioinspired optimization techniques, including Genetic Algorithm (GA) and Harris Hawks Optimization (HHO), for hyperparameter tuning. Layer pruning helps identify and eliminate less significant features, while model parameter optimization further enhances performance by fine-tuning critical hyperparameters, improving convergence speed,… More >

Federico Licastro^*

BIOCELL, Vol.49, No.6, pp. 981-989, 2025, DOI:10.32604/biocell.2025.062303 - 24 June 2025

Abstract Alzheimer’s disease (AD) is a neurodegenerative disease causing the most frequent form of dementia in old age. AD etiology is still uncertain and deposition of abnormal proteins in the brain along with chronic neuroinflammation have been suggested as pathogenic mechanisms of neuronal death. Infections by exogenous neurotropic virus, endogenous retrovirus reactivation, infections by other microbes, and air pollutants may either induce neurodegeneration or activate brain inflammation. Up to 8% of the human genome has a retroviral origin. These ancient retroviruses, also called human endogenous retroviruses, are associated with a clinical history of several neurodegenerative diseases.… More >

ARTEM P. GUREEV¹, IRINA S. SADOVNIKOVA¹, EKATERINA V. CHERNYSHOVA¹, EKATERINA P. KRUTSKIKH¹, IRINA B. PEVZNER², LJUBAVA D. ZOROVA², VERONIKA V. NESTEROVA¹, POLINA I. BABENKOVA¹, EGOR Y. PLOTNIKOV^2,*

BIOCELL, Vol.49, No.5, pp. 873-892, 2025, DOI:10.32604/biocell.2025.063557 - 27 May 2025

Abstract Background: Mitochondrial dysfunction plays a critical role in the pathogenesis of Alzheimer’s disease (AD). Resveratrol is a promising compound for the treatment of various neurodegenerative diseases, including AD. Aims: To investigate mitochondrial damage and the effects of resveratrol on inflammation, cognitive function, and mitochondrial quality control in APP/PS1 mice. Methods: Comparative analysis of mitochondrial DNA (mtDNA) damage was conducted between 10-month-old APP/PS1 mice and age-matched C57BL/6 mice. Assessments included measurement of amyloid-β levels, inflammatory markers, swimming distance in the Morris water maze, and gut microbiome composition. Resveratrol’s effects on cytokine expression, mtDNA levels in plasma, and… More >

Jasem Almotiri^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2109-2142, 2025, DOI:10.32604/cmc.2025.062923 - 16 April 2025

Abstract The evolving field of Alzheimer’s disease (AD) diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance (MR) images. This study introduces Dynamic GradNet, a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification. Initially, four state-of-the-art convolutional neural network (CNN) architectures, the self-regulated network (RegNet), residual network (ResNet), densely connected convolutional network (DenseNet), and efficient network (EfficientNet), were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation. Among these models, EfficientNet consistently demonstrated superior performance in terms of accuracy, precision, recall, and… More >