Virtual reality revolution in healthcare: a systematic review

Health and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

Language: Английский

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

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 187, P. 109767 - 109767

Published: Feb. 11, 2025

Language: 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, Journal Year: 2025, Volume and Issue: 194, P. 116269 - 116269

Published: March 7, 2025

Language: Английский

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

Information Sciences, Journal Year: 2024, Volume and Issue: 677, P. 120833 - 120833

Published: June 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.

Language: 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, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 45

Published: Feb. 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.

Language: 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, Journal Year: 2025, Volume and Issue: 145, P. 110281 - 110281

Published: Feb. 15, 2025

Language: 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, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 43

Published: March 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.

Language: 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, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Language: 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, Journal Year: 2025, Volume and Issue: 154, P. 111011 - 111011

Published: May 3, 2025

Language: Английский

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Cognitive driven gait freezing phase detection and classification for neuro-rehabilitated patients using machine learning algorithms

Journal of Neuroscience Methods, Journal Year: 2024, Volume and Issue: 409, P. 110183 - 110183

Published: June 3, 2024

Language: Английский

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