Medical Image Fusion for Multiple Diseases Features Enhancement DOI Open Access
Sajid Ullah Khan, Meshal Alharbi, Sajid Shah

и другие.

International Journal of Imaging Systems and Technology, Год журнала: 2024, Номер 34(6)

Опубликована: Окт. 17, 2024

ABSTRACT Throughout the past 20 years, medical imaging has found extensive application in clinical diagnosis. Doctors may find it difficult to diagnose diseases using only one modality. The main objective of multimodal image fusion (MMIF) is improve both accuracy and quality assessments by extracting structural spectral information from source images. This study proposes a novel MMIF method assist doctors postoperations such as segmentation, classification, further surgical procedures. Initially, intensity‐hue‐saturation (IHS) model utilized decompose positron emission tomography (PET)/single photon computed (SPECT) image, followed hue‐angle mapping discriminate high‐ low‐activity regions PET Then, proposed structure feature adjustment (SFA) mechanism used strategy for obtain anatomical details with minimum color distortion. In second step, new multi‐discriminator generative adversarial network (MDcGAN) approach obtaining final fused image. qualitative quantitative results demonstrate that superior existing methods preserving structural, anatomical, functional PET/SPECT Through our assessment, involving visual analysis subsequent verification statistical metrics, becomes evident changes contribute substantial MR outcomes that, majority cases, algorithm consistently outperformed other methods. Yet, few instances, achieved second‐highest results. validity was confirmed diverse modalities, encompassing total 1012 pairs.

Язык: Английский

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

Roshana Mukhtar,

Chuan‐Yu Chang, Ashwag Mohammed Mukhtar

и другие.

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

Опубликована: Март 7, 2025

Язык: Английский

Процитировано

1
ZF-QDCNN: ZFNet and quantum dilated convolutional neural network based Alzheimer's disease detection using MRI images DOI

Sharda Y. Salunkhe,

Mahesh S. Chavan

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

Процитировано

0
Intelligent exogenous networks with Bayesian distributed backpropagation for nonlinear single delay brain electrical activity rhythms in Parkinson's disease system DOI

Roshana Mukhtar,

Chuan‐Yu Chang, Muhammad Asif Zahoor Raja

и другие.

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

Опубликована: Фев. 15, 2025

Язык: Английский

Процитировано

0
Medical Image Fusion for Multiple Diseases Features Enhancement DOI Open Access
Sajid Ullah Khan, Meshal Alharbi, Sajid Shah

и другие.

International Journal of Imaging Systems and Technology, Год журнала: 2024, Номер 34(6)

Опубликована: Окт. 17, 2024

ABSTRACT Throughout the past 20 years, medical imaging has found extensive application in clinical diagnosis. Doctors may find it difficult to diagnose diseases using only one modality. The main objective of multimodal image fusion (MMIF) is improve both accuracy and quality assessments by extracting structural spectral information from source images. This study proposes a novel MMIF method assist doctors postoperations such as segmentation, classification, further surgical procedures. Initially, intensity‐hue‐saturation (IHS) model utilized decompose positron emission tomography (PET)/single photon computed (SPECT) image, followed hue‐angle mapping discriminate high‐ low‐activity regions PET Then, proposed structure feature adjustment (SFA) mechanism used strategy for obtain anatomical details with minimum color distortion. In second step, new multi‐discriminator generative adversarial network (MDcGAN) approach obtaining final fused image. qualitative quantitative results demonstrate that superior existing methods preserving structural, anatomical, functional PET/SPECT Through our assessment, involving visual analysis subsequent verification statistical metrics, becomes evident changes contribute substantial MR outcomes that, majority cases, algorithm consistently outperformed other methods. Yet, few instances, achieved second‐highest results. validity was confirmed diverse modalities, encompassing total 1012 pairs.

Язык: Английский

Процитировано

0