Опубликована: Сен. 18, 2024
Язык: Английский
Multimedia Tools and Applications, Год журнала: 2025, Номер unknown
Опубликована: Янв. 20, 2025
Язык: Английский
Процитировано
1
Frontiers in Neuroinformatics, Год журнала: 2024, Номер 18
Опубликована: Июнь 18, 2024
Background The Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans. Methods RViT incorporates rotated patch embeddings to enhance the accuracy of identification. Results Evaluation on Brain Tumor Dataset from Kaggle demonstrates RViT's superior performance with sensitivity (1.0), specificity (0.975), F1-score (0.984), Matthew's Correlation Coefficient (MCC) (0.972), and an overall 0.986. Conclusion outperforms standard several existing techniques, highlighting its efficacy in medical imaging. study confirms that integrating rotational improves model's capability handle diverse orientations, common challenge specialized architecture invariance approach have potential current methodologies detection extend other complex imaging tasks.
Язык: Английский
Процитировано
6
Опубликована: Янв. 3, 2025
In the fields of precision biometrics and healthcare, high-resolution biometric images play a crucial role as objective proof for accurate illness diagnosis. However, due to limitations in hardware resolution scanning duration, real-time acquisition biomedical poses challenges. Classic image super-resolution reconstruction (SRR) algorithms suffer from difficulties estimating model parameters, resulting blurry unrealistic reconstructed images, making them unsuitable images. To address this issue, chapter proposes sparse-coding nonlocal attention dual-network. By employing mechanisms, Gaussian constraints, parameter sharing strategies up-sampling down-sampling dual branches, SRR is achieved. It has high signal-to-noise ratio 30.84 dB structural identity 0.914 rebuilt The research shows that suggested method not only correctly reconstructs details at frequency but it also improves modeling efficiency with lightweight mechanisms. This makes useful reconstructing very resolutions biometrics.
Язык: Английский
Процитировано
0
Опубликована: Янв. 3, 2025
This study presents a sophisticated ResNet-10 convolutional neural network model that is specifically developed to address the classification difficulties of brain computed tomography (CT) images, particularly those associated with Alzheimer's disease (AD), lesions (including tumors), and normal aging in smart healthcare. The employs residual hybrid attention module (RHAM) enhance specificity features, enabling it effectively collect both spatial information relevant content within tissue. These enhancements model's efficacy traditional categorization tumor diagnosis through utilization associative learning interpretable generative artificial intelligence (GAI). To streamline intricacy design, global media collecting layer implemented, dropout mechanism incorporated subsequent levels prevent unnecessary installation. Throughout training, this makes use label smoothing entropy loss functions its capacity for generalization, even limited quantity training samples. advanced has been extensively tested proven effective on CT scans, obtaining an incredible 97.47% accuracy. demonstration emphasized potential application broader domains such as GAI-based collaborative detection.
Язык: Английский
Процитировано
0
Computer Vision and Image Understanding, Год журнала: 2025, Номер unknown, С. 104324 - 104324
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0
Asian Journal of Current Research, Год журнала: 2024, Номер 9(3), С. 181 - 195
Опубликована: Авг. 10, 2024
Neuroscience, a dynamic field at the forefront of scientific exploration, is unravelling complexities human brain. By merging biology, psychology, physics, and computer science, researchers are gaining profound insights into cognition, behaviour, neurological underpinnings diseases. Brain mapping key component recent advancements. Techniques like fMRI, PET, DTI offer unprecedented views brain structure function. The Human Connectome Project similar initiatives have produced detailed maps connections, revealing how different regions interact to support cognition behaviour. These crucial for identifying disease biomarkers, predicting treatment responses, developing targeted therapies. Molecular biology genetics also driving progress. Researchers uncovering genetic basis disorders, providing clues about susceptibility progression. imaging techniques visualise neurotransmitter systems cellular processes, shedding light on mechanisms. integration neuroscience with modelling AI revolutionising research. algorithms analyse vast datasets, simulate neural networks, even decode signals brain-machine interfaces. This has potential personalised medicine ground-breaking treatments. future holds immense promise. optogenetics single-cell will greater precision in studying circuits. However, we must address ethical considerations around data privacy, cognitive enhancement, brain-altering interventions. Neuroscience not just understanding brain; it's improving lives. striving conquer disorders maximize by pushing boundaries knowledge technology while upholding principles.
Язык: Английский
Процитировано
3
Опубликована: Апрель 26, 2024
This study introduces a novel approach to regional nerve block anesthesia in scapular fractures by integrating ultrasound guidance with deep learning techniques. The proposed method leverages advanced imaging modalities and state-of-the-art models achieve precise localization amidst complex fracture patterns. Through extensive experimentation evaluation, we demonstrate the effectiveness of enhancing surgical planning, intraoperative decision-making, patient safety. represents significant advancement field orthopedic surgery, offering promising solution for improving outcomes quality life patients fractures. this suggests locating nerves shoulder advances orthopaedic surgery. We can improve, expedite, streamline procedures use AI medical imaging. will improve
Язык: Английский
Процитировано
0
Опубликована: Окт. 3, 2024
Язык: Английский
Процитировано
0
Cuadernos de Educación y Desarrollo, Год журнала: 2024, Номер 16(13), С. e7065 - e7065
Опубликована: Дек. 23, 2024
Introdução: A segmentação de tumores cerebrais, como meningiomas e gliomas, em imagens ressonância magnética (RM) é essencial para diagnóstico, planejamento cirúrgico terapias, mas enfrenta desafios complexos. Meningiomas, os cerebrais primários mais comuns, requerem delineamento preciso devido à sua morfologia variada, localização anatômica. Objetivo: Compreender o pré-processamento automatizado das tumorais, contribuindo mitigar erros segmentação, desta forma, as técnicas avançadas redes neurais convolucionais (CNNs) métodos tradicionais terão coesão na precisão da clínica diagnóstica. Resultado: O desafio Brain Tumor Segmentation (BraTS), iniciado 2012, desempenhou papel crucial ao fornecer bases dados multimodais promover desenvolvimento algoritmos segmentação. iniciativa expandiu seu foco incluir meningiomas. Com envolvimento equipes internacionais, resultados destacaram promissores podendo influenciar diretamente manejo clínico, planejamentos radioterapia decisões cirúrgicas. relevância segmentações precisas não apenas melhorar técnicos, também impactar positivamente tratamento do paciente. Conclusão: avanço análise médicas com IA só eleva a diagnóstica contribui padrões cuidado eficientes personalizados. Este campo continua evoluindo, estabelecendo novos referenciais automatizada cerebrais.
Процитировано
0
Опубликована: Сен. 18, 2024
Язык: Английский
Процитировано
0