Detecting mild traumatic brain injury for athletes using SSVEP classification: A case study

Hoang, Quang Thien; Yong, Ken-Tye; Liu, Xiaochen; Mahony, Dylan; Chaitarvornkit, Alissa; Cohen, Adrian; Grootswagers, Tijl

doi:10.1016/j.bspc.2023.105274

Cited by 3 publications

(1 citation statement)

References 73 publications

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“…Recently, in many studies, CNNs have been widely employed to classify brain MRIs, and are validated on a different dataset of brain tumors [20][21][22][23][24][25][26]. A deep CNN-based model was proposed in [27] for brain MRI image categorization into distinct classes.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN

Zahoor,

Khan,

Alahmadi

et al. 2024

Biomedicines

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Brain tumor classification is essential for clinical diagnosis and treatment planning. Deep learning models have shown great promise in this task, but they are often challenged by the complex and diverse nature of brain tumors. To address this challenge, we propose a novel deep residual and region-based convolutional neural network (CNN) architecture, called Res-BRNet, for brain tumor classification using magnetic resonance imaging (MRI) scans. Res-BRNet employs a systematic combination of regional and boundary-based operations within modified spatial and residual blocks. The spatial blocks extract homogeneity, heterogeneity, and boundary-related features of brain tumors, while the residual blocks significantly capture local and global texture variations. We evaluated the performance of Res-BRNet on a challenging dataset collected from Kaggle repositories, Br35H, and figshare, containing various tumor categories, including meningioma, glioma, pituitary, and healthy images. Res-BRNet outperformed standard CNN models, achieving excellent accuracy (98.22%), sensitivity (0.9811), F1-score (0.9841), and precision (0.9822). Our results suggest that Res-BRNet is a promising tool for brain tumor classification, with the potential to improve the accuracy and efficiency of clinical diagnosis and treatment planning.

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Section: Introductionmentioning

confidence: 99%

Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN

Zahoor,

Khan,

Alahmadi

et al. 2024

Biomedicines

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Colorimetry/fluorescence dual-mode detection of Salmonella typhimurium based on a “three-in-one” nanohybrid with high oxidase-like activity for AIEgen

Li,

Xu,

Shi

et al. 2024

Food Chemistry

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Psychometric Properties of Vestibular and Ocular Measures Used for Concussion Assessments: A Scoping Review

Barnes,

Sveistrup,

Karimijashni

et al. 2024

Journal of Head Trauma Rehabilitation

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Background: Concussions most commonly affect the vestibular and ocular systems. Clinical measures used in the assessment of vestibular and ocular deficits should contain strong psychometric properties so that clinicians can accurately detect abnormality to guide treatment interventions. Objectives: The aim of this scoping review was: (1) to identify the measures used to evaluate the vestibular and ocular domains postconcussion and (2) to document the psychometric properties of the measures. Methods: Two databases (Medline (Ovid) and Embase) were searched from inception to May 2023. An updated search was completed in January 2024 using the same databases and search terms. Studies were screened and data were extracted independently by 2 reviewers. Measures were categorized into vestibular, ocular, or both (vestibular and ocular) domains, and relevant psychometric properties were documented. Results: Fifty-two studies were included in this review. 28 studies explored the use of vestibular measures, 12 explored ocular measures, and 12 explored both vestibular and ocular measures or explored the use of vestibulo-ocular reflex measures. Most studies explored the properties associated with balance measures, particularly the balance error scoring system. Diagnostic accuracy (sensitivity and specificity metrics) of the associated measures was the most frequently documented characteristic in the literature. Conclusion: Identification of clinical measures used to evaluate vestibular and ocular deficits postconcussion is needed to understand the evidence supporting their use in practice. Documenting the psychometric properties will allow clinicians and researchers to understand the status of the current literature and support for the use of certain measures in practice in terms of their ability to appropriately detect deficits in people with concussion when deficits are truly present.

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Detecting mild traumatic brain injury for athletes using SSVEP classification: A case study

Cited by 3 publications

References 73 publications

Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN

Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN

Colorimetry/fluorescence dual-mode detection of Salmonella typhimurium based on a “three-in-one” nanohybrid with high oxidase-like activity for AIEgen

Psychometric Properties of Vestibular and Ocular Measures Used for Concussion Assessments: A Scoping Review

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