Virtual reality revolution in healthcare: a systematic review

Health and Technology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 10, 2025

Язык: Английский

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Review on computational methods for the detection and classification of Parkinson's Disease

Computers in Biology and Medicine, Год журнала: 2025, Номер 187, С. 109767 - 109767

Опубликована: Фев. 11, 2025

Язык: Английский

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Hybrid ladybug Hawk optimization-enabled deep learning for multimodal Parkinson’s disease classification using voice signals and hand-drawn images

Network Computation in Neural Systems, Год журнала: 2025, Номер unknown, С. 1 - 43

Опубликована: Март 4, 2025

PD is a progressive neurodegenerative disorder that leads to gradual motor impairments. Early detection critical for slowing the disease's progression and providing patients access timely therapies. However, accurately detecting in its early stages remains challenging. This study aims develop an optimized deep learning model classification using voice signals hand-drawn spiral images, leveraging ZFNet-LHO-DRN. The proposed first preprocesses input signal Gaussian filter remove noise. Features are then extracted from preprocessed passed ZFNet generate output-1. For image, preprocessing performed with bilateral filter, followed by image augmentation. Here also, features forwarded DRN form output-2. Both classifiers trained LHO algorithm. Finally, output-1 output-2, best one selected based on majority voting. ZFNet-LHO-DRN demonstrated excellent performance achieving premium accuracy of 89.8%, NPV 89.7%, PPV TNR 89.3%, TPR 90.1%. model's high indicate potential as valuable tool assisting diagnosis PD.

Язык: Английский

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A novel fractional Parkinson's disease onset model involving α-syn neuronal transportation and aggregation: A treatise on machine predictive networks

Chaos Solitons & Fractals, Год журнала: 2025, Номер 194, С. 116269 - 116269

Опубликована: Март 7, 2025

Язык: Английский

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Physically interpretable discrete latent representations for the design of advanced mechanical metamaterials in complex geometries

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 154, С. 111011 - 111011

Опубликована: Май 3, 2025

Язык: Английский

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FDCNN-AS: Federated deep convolutional neural network Alzheimer detection schemes for different age groups

Information Sciences, Год журнала: 2024, Номер 677, С. 120833 - 120833

Опубликована: Июнь 5, 2024

Alzheimer's disease (AD) is a memory-related that occurs in the human brain where neurons become degenerative. It an evolved form of dementia deteriorates over time. Machine learning, extended version deep has appeared as optimistic strategy for AD detection. Regardless, existing detection approaches have yet to acquire expected accuracy, mainly due unreasonable data training and testing. In this paper, we present Federated Deep Convolutional Neural Network Alzheimer Detection Schemes (FDCNN-AS), specifically designed varying age groups. FDCNN-AS efficient framework contains architecture, algorithm flow, implementation. manages from various laboratories processes it additional clinics. Our method mixes models different types such positron emission tomography, summed magnetic resonance imaging, blood tests, questionnaires about synaptic degeneration. Further, look at some restrictions be addressed These include seeing ages, extrapolating severity damage, comparing treatment recovery rates, finding benign malignant ranges been collected. To ensure secure privacy-preserving execute within federated learning environment concerns considerable Within setup, operate generic convolutional neural network. The experimental results indicate performs optimally, reaching remarkable 99% accuracy detecting brain.

Язык: Английский

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ZF-QDCNN: ZFNet and quantum dilated convolutional neural network based Alzheimer's disease detection using MRI images

Network Computation in Neural Systems, Год журнала: 2025, Номер unknown, С. 1 - 45

Опубликована: Фев. 11, 2025

Alzheimer's disease (AD) is a severe neurological disorder that leads to irreversible memory loss. In the previous research, early-stage often presents with subtle issues are difficult differentiate from normal age-related changes. This research designed novel detection model called Zeiler and Fergus Quantum Dilated Convolutional Neural Network (ZF-QDCNN) for AD using Magnetic Resonance Imaging (MRI). Initially, input MRI images taken specific dataset, which pre-processed Gaussian filter. Then, brain area segmentation performed by utilizing Channel-wise Feature Pyramid Medicine (CFPNet-M). After segmentation, relevant features extracted, classification of ZF-QDCNN, integration (ZFNet) (QDCNN). Moreover, ZF-QDCNN demonstrated promising performance, achieving an accuracy 91.7%, sensitivity 90.7%, specificity 92.7%, f-measure 91.8% in detecting AD. Additionally, proposed effectively identifies classifies images, highlighting its potential as valuable tool early diagnosis management condition.

Язык: Английский

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Intelligent exogenous networks with Bayesian distributed backpropagation for nonlinear single delay brain electrical activity rhythms in Parkinson's disease system

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 145, С. 110281 - 110281

Опубликована: Фев. 15, 2025

Язык: Английский

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An efficient Parkinson's disease detection using smoothed pseudo-Wigner Ville distribution and MobileNetV2 convolutional neural network

Health and Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Язык: Английский

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Explainable convolutional neural network for Parkinson's disease detection

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 451 - 476

Опубликована: Янв. 1, 2025

Язык: Английский

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