Methods for Detecting the Patient’s Pupils’ Coordinates and Head Rotation Angle for the Video Head Impulse Test (vHIT), Applicable for the Diagnosis of Vestibular Neuritis and Pre-Stroke Conditions DOI Creative Commons

G. D. Mamykin,

А. А. Кулеш, F. L. Barkov

et al.

Computation, Journal Year: 2024, Volume and Issue: 12(8), P. 167 - 167

Published: Aug. 18, 2024

In the contemporary era, dizziness is a prevalent ailment among patients. It can be caused by either vestibular neuritis or stroke. Given lack of diagnostic utility instrumental methods in acute isolated vertigo, differentiation and stroke primarily clinical. As part initial differential diagnosis, physician focuses on characteristics nystagmus results video head impulse test (vHIT). Instruments for accurate vHIT are costly often utilized exclusively healthcare settings. The objective this paper to review methodologies accurately detecting position pupil centers both eyes patient precisely extracting their coordinates. Additionally, describes determining rotation angle under diverse imaging lighting conditions. Furthermore, suitability these being evaluated. We assume maximum allowable error 0.005 radians per frame detect pupils’ coordinates 0.3 degrees while position. found that such conditions, most suitable approaches posture detection deep learning (including LSTM networks), search template matching, linear regression EMG sensor data, optical fiber usage. relevant localization our medical tasks learning, geometric transformations, decision trees, RASNAC. This study might assist identification number employed future construct high-accuracy system based smartphone home computer, with subsequent signal processing diagnosis.

Language: Английский

SAlexNet: Superimposed AlexNet using Residual Attention Mechanism for Accurate and Efficient Automatic Primary Brain Tumor Detection and Classification DOI Creative Commons

Qurat-ul-ain Chaudhary,

Shahzad Ahmad Qureshi,

Touseef Sadiq

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104025 - 104025

Published: Jan. 1, 2025

Language: Английский

Citations

3
Dynamic Focus on Tumor Boundaries: A Lightweight U-Net for MRI Brain Tumor Segmentation DOI Creative Commons

Kuldashboy Avazov,

Sanjar Mirzakhalilov, Sabina Umirzakova

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(12), P. 1302 - 1302

Published: Dec. 23, 2024

Accurate segmentation of brain tumors in MRI scans is critical for diagnosis and treatment planning. Traditional models, such as U-Net, excel capturing spatial information but often struggle with complex tumor boundaries subtle variations image contrast. These limitations can lead to inconsistencies identifying regions, impacting the accuracy clinical outcomes. To address these challenges, this paper proposes a novel modification U-Net architecture by integrating attention mechanism designed dynamically focus on relevant regions within scans. This innovation enhances model's ability delineate fine improves precision. Our model was evaluated Figshare dataset, which includes annotated images meningioma, glioma, pituitary tumors. The proposed achieved Dice similarity coefficient (DSC) 0.93, recall 0.95, an AUC 0.94, outperforming existing approaches V-Net, DeepLab V3+, nnU-Net. results demonstrate effectiveness our addressing key challenges like low-contrast boundaries, small overlapping Furthermore, lightweight design ensures its suitability real-time applications, making it robust tool automated segmentation. study underscores potential mechanisms significantly enhance medical imaging models paves way more effective diagnostic tools.

Language: Английский

Citations

7
Alzheimer's Disease Prediction Using 3D-CNNs: Intelligent Processing of Neuroimaging Data DOI Creative Commons
Atta Ur Rahman,

Sania Ali,

Bibi Saqia

et al.

SLAS TECHNOLOGY, Journal Year: 2025, Volume and Issue: unknown, P. 100265 - 100265

Published: March 1, 2025

Language: Английский

Citations

0
A novel similarity navigated graph neural networks and crayfish optimization algorithm for accurate brain tumor detection DOI

A. Padmashree,

P.V. Sankar,

Ahmad Alkhayyat

et al.

Research on Biomedical Engineering, Journal Year: 2025, Volume and Issue: 41(2)

Published: April 5, 2025

Language: Английский

Citations

0
AI in MRI Brain Tumor Diagnosis: A Systematic Review of Machine Learning and Deep Learning Advances (2010–2025) DOI

Vaidehi Satushe,

Vibha Vyas,

Shilpa P. Metkar

et al.

Chemometrics and Intelligent Laboratory Systems, Journal Year: 2025, Volume and Issue: 263, P. 105414 - 105414

Published: April 25, 2025

Language: Английский

Citations

0
Convolutional Neural Network Incorporating Multiple Attention Mechanisms for MRI Classification of Lumbar Spinal Stenosis DOI Creative Commons
Juncai Lin, Honglai Zhang, Hongcai Shang

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(10), P. 1021 - 1021

Published: Oct. 13, 2024

Lumbar spinal stenosis (LSS) is a common cause of low back pain, especially in the elderly, and accurate diagnosis critical for effective treatment. However, manual using MRI images time consuming subjective, leading to need automated methods.

Language: Английский

Citations

2
Accelerating Brain MR Imaging With Multisequence and Convolutional Neural Networks DOI Creative Commons
Zhanhao Mo,

He Sui,

Zhongwen Lv

et al.

Brain and Behavior, Journal Year: 2024, Volume and Issue: 14(11)

Published: Nov. 1, 2024

Magnetic resonance imaging (MRI) refers to one of the critical image modalities for diagnosis, whereas its long acquisition time limits application. In this study, aim was investigate whether deep learning-based techniques are capable using common information in different MRI sequences reduce scan most time-consuming while maintaining quality.

Language: Английский

Citations

1
UV Hyperspectral Imaging with Xenon and Deuterium Light Sources: Integrating PCA and Neural Networks for Analysis of Different Raw Cotton Types DOI Creative Commons
Mohammad Al Ktash, Mona Knoblich,

Max Eberle

et al.

Journal of Imaging, Journal Year: 2024, Volume and Issue: 10(12), P. 310 - 310

Published: Dec. 5, 2024

Ultraviolet (UV) hyperspectral imaging shows significant promise for the classification and quality assessment of raw cotton, a key material in textile industry. This study evaluates efficacy UV (225–408 nm) using two different light sources: xenon arc (XBO) deuterium lamps, comparison to NIR imaging. The aim is determine which source provides better differentiation between cotton types imaging, as each interacts differently with materials, potentially affecting accuracy. Principal component analysis (PCA) Quadratic Discriminant Analysis (QDA) were employed differentiate various hemp plant. PCA XBO illumination revealed that first three principal components (PCs) accounted 94.8% total variance: PC1 (78.4%) PC2 (11.6%) clustered samples into four main groups—hemp (HP), recycled (RcC), organic (OC) from other samples—while PC3 (6%) further separated RcC. When source, PCs explained 89.4% variance, effectively distinguishing sample such HP, RcC, OC remaining samples, clearly separating combining scores QDA, accuracy reached 76.1% 85.1% source. Furthermore, deep learning technique called fully connected neural network was applied. sources 83.6% 90.1%, respectively. results highlight ability this method conventional well hemp, identify distinct suggesting varying recycling processes possible common origins cotton. These findings underscore potential coupled chemometric models, powerful tool enhancing

Language: Английский

Citations

1
Methods for Detecting the Patient’s Pupils’ Coordinates and Head Rotation Angle for the Video Head Impulse Test (vHIT), Applicable for the Diagnosis of Vestibular Neuritis and Pre-Stroke Conditions DOI Creative Commons

G. D. Mamykin,

А. А. Кулеш, F. L. Barkov

et al.

Computation, Journal Year: 2024, Volume and Issue: 12(8), P. 167 - 167

Published: Aug. 18, 2024

In the contemporary era, dizziness is a prevalent ailment among patients. It can be caused by either vestibular neuritis or stroke. Given lack of diagnostic utility instrumental methods in acute isolated vertigo, differentiation and stroke primarily clinical. As part initial differential diagnosis, physician focuses on characteristics nystagmus results video head impulse test (vHIT). Instruments for accurate vHIT are costly often utilized exclusively healthcare settings. The objective this paper to review methodologies accurately detecting position pupil centers both eyes patient precisely extracting their coordinates. Additionally, describes determining rotation angle under diverse imaging lighting conditions. Furthermore, suitability these being evaluated. We assume maximum allowable error 0.005 radians per frame detect pupils’ coordinates 0.3 degrees while position. found that such conditions, most suitable approaches posture detection deep learning (including LSTM networks), search template matching, linear regression EMG sensor data, optical fiber usage. relevant localization our medical tasks learning, geometric transformations, decision trees, RASNAC. This study might assist identification number employed future construct high-accuracy system based smartphone home computer, with subsequent signal processing diagnosis.

Language: Английский

Citations

0