An intellectual autism spectrum disorder classification framework in healthcare industry using ViT-based adaptive deep learning model

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 106, С. 107737 - 107737

Опубликована: Март 3, 2025

Язык: Английский

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Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 16, 2025

The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these remains a challenge due to their inherent complexity non-linearity. This study introduces novel method, PCA-ANFIS, which integrates Principal Component Analysis (PCA) Adaptive Neuro-Fuzzy Inference Systems (ANFIS), enhance pattern recognition in multimodal signal analysis. PCA reduces the dimensionality EEG data while retaining salient features, enabling computational efficiency. ANFIS combines adaptability neural networks with interpretability fuzzy logic, making it well-suited model non-linear relationships within signals. Performance metrics our proposed such as accuracy, sensitivity, These additions highlight effectiveness method provide concise summary findings. achieves superior classification performance, an unprecedented accuracy 99.5%, significantly outperforming existing approaches. Comprehensive experiments were conducted using diverse dataset, demonstrating method's robustness sensitivity. integration addresses key challenges analysis, artifact contamination non-stationarity, ensuring reliable feature extraction classification. research has significant implications for both neuroscience clinical practice. By advancing understanding processes, PCA-ANFIS facilitates accurate diagnosis treatment disorders neurological conditions. Future work will focus on testing approach larger more datasets exploring its applicability domains neurofeedback, neuromarketing, brain-computer interfaces. establishes capable tool precise efficient processing.

Язык: Английский

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Improved A-Line and B-Line Detection in Lung Ultrasound Using Deep Learning with Boundary-Aware Dice Loss

Bioengineering, Год журнала: 2025, Номер 12(3), С. 311 - 311

Опубликована: Март 18, 2025

Lung ultrasound (LUS) is a non-invasive bedside imaging technique for diagnosing pulmonary conditions, especially in critical care settings. A-lines and B-lines are important features LUS images that help to assess lung health identify changes tissue. However, accurately detecting segmenting these lines remains challenging, due their subtle blurred boundaries. To address this, we propose TransBound-UNet, novel segmentation model integrates transformer-based encoder with boundary-aware Dice loss enhance medical image segmentation. This function incorporates boundary-specific penalties into hybrid Dice-BCE formulation, allowing more accurate of structures. The proposed framework was tested on dataset 4599 images. achieved Score 0.80, outperforming state-of-the-art networks. Additionally, it demonstrated superior performance Specificity (0.97) Precision (0.85), significantly reduced Hausdorff Distance 15.13, indicating improved boundary delineation overall quality. Post-processing techniques were applied automatically detect count B-lines, demonstrating the potential segmented outputs diagnostic workflows. provides an efficient solution automated interpretation, precision.

Язык: Английский

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Class imbalance-aware domain specific transfer learning approach for medical image classification: Application on COVID-19 detection

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 150, С. 110583 - 110583

Опубликована: Март 21, 2025

Язык: Английский

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Advancements and challenges in CT image segmentation for COVID-19 diagnosis through augmented and virtual Reality: A systematic review and future perspectives

Journal of Radiation Research and Applied Sciences, Год журнала: 2025, Номер 18(2), С. 101374 - 101374

Опубликована: Март 23, 2025

Язык: Английский

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A missing multimodal imputation diffusion model for 2D X-ray and 3D CT in COVID-19 diagnosis

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127367 - 127367

Опубликована: Март 1, 2025

Язык: Английский

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Real-Time Robbery Detection in Public Transport Using Audio Recordings and Deep Learning

SN Computer Science, Год журнала: 2025, Номер 6(4)

Опубликована: Апрель 1, 2025

Язык: Английский

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Quantum-inspired feature extraction model from EEG frequency waves for enhanced schizophrenia detection

Chaos Solitons & Fractals, Год журнала: 2025, Номер 196, С. 116401 - 116401

Опубликована: Апрель 7, 2025

Язык: Английский

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Advancing COVID-19 data classification and prediction: A fresh perspective from an ontological machine–learning algorithm

Expert Systems with Applications, Год журнала: 2025, Номер 279, С. 127592 - 127592

Опубликована: Апрель 8, 2025

Язык: Английский

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Computer-based quantitative image texture analysis using multi-collinearity diagnosis in chest X-ray images

PLoS ONE, Год журнала: 2025, Номер 20(4), С. e0320706 - e0320706

Опубликована: Апрель 14, 2025

Despite tremendous efforts devoted to the area, image texture analysis is still an open research field. This paper presents algorithm and experimental results demonstrating feasibility of developing automated tools detect abnormal X-ray images based on tissue attenuation. Specifically, this work proposes using variability characterised by singular values conditional indices extracted from value decomposition (SVD) as features. In addition, introduces a “tuning weight" parameter consider attenuation in tissues affected pathologies. weight estimated coefficient variation minimum covariance determinant bandwidth yielded non-parametric distribution variance-decomposition proportions SVD. When multiplied two features (singular indices), single acts tuning weight, reducing misclassification improving classic performance metrics, such true positive rate, false negative predictive values, discovery area-under-curve, accuracy total cost. The proposed method implements ensemble bagged trees classification model classify chest COVID-19, viral pneumonia, lung opacity, or normal. It was tested challenging, imbalanced public dataset. show 88% without applying 99% with its application. outperforms state-of-the-art methods, attested all metrics.

Язык: Английский

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