Suchergebnisse - "Stroke classification"

Platelet Indices in Distinguishing Thrombotic vs Embolic Ischemic Stroke

Autoren: Suleyman Alpar Muhammed Furkan Özden Ali Cankut Tatliparmak et al.

Quelle: Volume: 12, Issue: 2296-304
Celal Bayar Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi
CBU-SBED: Celal Bayar University-Health Sciences Institute Journal

Schlagwörter: Clinical Sciences (Other), İskemik inme, trombotik inme, embolik inme, trombosit indeksleri, inme sınıflandırması, ortalama trombosit hacmi, trombosit dağılım genişliği, inme patofizyolojisi, Ischemic stroke, thrombotic stroke, embolic stroke, platelet indices, stroke classification, mean platelet volume, platelet distribution width, stroke pathophysiology, Klinik Tıp Bilimleri (Diğer)

Dateibeschreibung: application/pdf

Zugangs-URL: https://dergipark.org.tr/tr/pub/cbusbed/issue/92887/1667940

A Comprehensive Deep Learning Framework for Microwave Stroke Classification: Combining Signal Analysis, Clinical Variables, and Antenna System Measurements

Autoren: Wei Yuan Phawis Thammasorn Lingxiao Wang et al.

Quelle: IEEE Access, Vol 13, Pp 39935-39949 (2025)

Schlagwörter: stroke classification, Deep learning, Electrical engineering. Electronics. Nuclear engineering, microwave signal processing, health care, medical image analysis, TK1-9971

Zugangs-URL: https://doaj.org/article/49d475d22e304cb28e43445ff4300995

Embolic Stroke of Undetermined Source (ESUS): Prevalence, Risk Factors, and Diagnostic Insights from a Prospective German Cohort

Autoren: Priyanka Boettger Jamschid Sedighi Martin Juenemann et al.

Quelle: Annals of Indian Academy of Neurology, Vol 28, Iss 5, Pp 684-692 (2025)

Schlagwörter: esus, embolic stroke, cryptogenic stroke, stroke subtype comparison, stroke classification, secondary prevention, Neurology. Diseases of the nervous system, RC346-429

Dateibeschreibung: electronic resource

Relation: https://journals.lww.com/10.4103/aian.aian_327_25; https://doaj.org/toc/0972-2327; https://doaj.org/toc/1998-3549

Zugangs-URL: https://doaj.org/article/ca228c3ae3ad4164a8a0472abe8e8ca0

A novel machine learning framework for stroke type identification in resource constrained settings with robustness to missing data

Autoren: Aman Bhardwaj Yamini Antil M. V. Padma Srivastava et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-16 (2025)

Schlagwörter: Machine learning, Stroke classification, Resource limited settings, Multiple imputation by chained equation, MICE, SHAP, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/ba57daa4281d4398a05c9af80a6d40b7

Multi-dimensional feature extraction of EEG signal and its application in stroke classification

Autoren: Teng Wang Wenhui Jia Fenglian Li et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-17 (2025)

Schlagwörter: Stroke classification, EEG, Autocorrelation feature, Complexity feature, Fuzzy membership function, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/190ed4de253c4a1384e43ba3d08f2094

Neuroimaging-Driven Stroke Diagnosis: A Deep Learning Approach

Autoren: Yarrapureddy Madhu Smitha Dr. K G Chiranjivi

Schlagwörter: Machine Learning, Healthcare AI, Deep Learning, Medical Imaging, Neuroimages, Stroke Diagnosis, DenseNet, Stroke Classification, Diagnostic Model

Advancing ischemic stroke diagnosis and clinical outcome prediction using improved ensemble techniques in DSC-PWI radiomics

Autoren: Mazen M. Yassin Jiaxi Lu Asim Zaman et al.

Quelle: Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)

Schlagwörter: Stroke classification, NIHSS prediction, Dynamic radiomics features, DSC-PWI., Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/f05cad84c4ff4cc896b1cf8bac7a2700

Brain Stroke Classification Using CT Scans with Transformer-Based Models and Explainable AI

Autoren: Shomukh Qari Maha A. Thafar

Quelle: Diagnostics, Vol 15, Iss 19, p 2486 (2025)

Schlagwörter: hemorrhagic stroke, ischemic stroke, stroke classification, deep learning, transfer learning, vision transformer, Medicine (General), R5-920

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2075-4418/15/19/2486; https://doaj.org/toc/2075-4418

Zugangs-URL: https://doaj.org/article/3540b028a4f042c999ca52d50ce69500

An Explainable AI Framework for Stroke Classification Based on CT Brain Images

Autoren: Serra Aksoy Pinar Demircioglu Ismail Bogrekci

Quelle: AI, Vol 6, Iss 9, p 202 (2025)

Schlagwörter: stroke classification, deep learning, explainable AI, XRAI, clinical decision support, CT imaging, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2673-2688/6/9/202; https://doaj.org/toc/2673-2688

Zugangs-URL: https://doaj.org/article/60cdcfa933d84d67971e17cc8f297c00

Real-Time Paddle Stroke Classification and Wireless Monitoring in Open Water Using Wearable Inertial Nodes

Autoren: Vladut-Alexandru Dobra Ionut-Marian Dobra Silviu Folea

Quelle: Sensors, Vol 25, Iss 17, p 5307 (2025)

Schlagwörter: paddle stroke classification, wearable sensors, real-time feedback, ESP32, MPU6050, ESP-NOW, Chemical technology, TP1-1185

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/1424-8220/25/17/5307; https://doaj.org/toc/1424-8220

Zugangs-URL: https://doaj.org/article/1130b825e681465193052f563768c32f

Deep Learning-Based Classification of Stroke MRI Scans using Convolutional Neural Networks (CNNs)

Autoren: Sambhav Gupta

Quelle: International Journal of Sciences and Innovation Engineering; Vol. 2 No. 6 (2025): IJSCI VOLUME-02 ISSUE-06 JUNE 2025; 831-839 ; 3049-0251 ; 10.70849/

Schlagwörter: Stroke Classification, Deep Learning, CT Scans, Convolutional Neural Networks, Image Augmentation, Medical Imaging

Dateibeschreibung: application/pdf

Relation: https://ijsci.com/index.php/home/article/view/302/264; https://ijsci.com/index.php/home/article/view/302

Verfügbarkeit: https://ijsci.com/index.php/home/article/view/302
https://doi.org/10.70849/IJSCI

Beyin Bilgisayarlı Tomografi Görüntülerinde Görü Dönüştürücüler ve GAN Tabanlı Veri Artırma Kullanılarak İnme Sınıflandırması

Autoren: Yelken, Erdem Ceylan, Murat

Quelle: Volume: 37, Issue: 1387-400
Fırat Üniversitesi Mühendislik Bilimleri Dergisi
Fırat University Journal of Engineering Science

Schlagwörter: İnme sınıflandırması, görü dönüştürücüler, BT görüntüleme, veri artırma, derin öğrenme, Artificial Intelligence (Other), Deep Learning, Yapay Görme, Derin Öğrenme, Machine Vision, Biyomedikal Görüntüleme, Stroke classification, vision transformers, CT imaging, data augmentation, deep learning, Yapay Zeka (Diğer), Biomedical Imaging

Dateibeschreibung: application/pdf

Zugangs-URL: https://dergipark.org.tr/tr/pub/fumbd/issue/88821/1598597

A Quantitative Microwave Imaging Approach for Brain Stroke Classification Based on the Generalized Tikhonov Regularization

Autoren: Sayyed Saleh Sayyed Mousavi Mohammad Saeed Majedi

Quelle: IEEE Access, Vol 11, Pp 73370-73376 (2023)

Schlagwörter: Microwave imaging, Electrical engineering. Electronics. Nuclear engineering, generalized Tikhonov regularization, brain stroke classification, TK1-9971, 3. Good health

Zugangs-URL: https://doaj.org/article/7f95ef2700d948cc9a272738ab692dce

Four-way Classification of Tabla Strokes with Transfer Learning Using Western Drums

Autoren: Rohit M. Ananthanarayana Amitrajit Bhattacharjee Preeti Rao

Quelle: Transactions of the International Society for Music Information Retrieval, Vol 6, Iss 1, Pp 103–116-103–116 (2023)

Schlagwörter: stroke classification, automatic drum transcription, M1-5000, Information technology, transfer learning, T58.5-58.64, Music, tabla bols, data augmentation

Zugangs-URL: https://doaj.org/article/2e15d6d510a94c66bd6ea8390800ae3b

Integrating Algorithms with Intuitive AI to Forecast the Likelihood of Cerebral Infarction in Patients Exhibiting Signs of Illness

Autoren: Bhuvana R.

Quelle: International Journal of Intelligent Systems and Applications in Engineering; Vol. 12 No. 4 (2024); 1030-1034

Schlagwörter: Stroke, Ischemic stroke, Hemorrhagic stroke, Stroke classification, Artificial Intelligence algorithm , Normalized pointwise mutual information , Cerebrovascular disease. Random Forest , cluster grouping

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.ijisae.org/index.php/IJISAE/article/view/6325

Monitorización e clasificación de golpes de tenis con sensores inerciais e aprendizaxe máquina

Autoren: Barcia Facal, Julián Vázquez, Carlos Universidade da Coruña. Facultade de Informática

Weitere Verfasser: Barcia Facal, Julián Vázquez, Carlos Universidade da Coruña. Facultade de Informática

Schlagwörter: Clasificación de golpes de tenis, Aprendizaxe automática, Sensores inerciais, Wearable technology, Tennis stroke classification, Análise de movementos deportivos, Inertial sensors, Real-time monitoring, Data labeling, Aplicación Android, Etiquetaxe de datos, Micro-controladores, Android application, Sincronización de datos de sensores, Machine learning, Sports movement analysis, Monitorización en tempo real, Tecnoloxía portátil, Microcontrollers, Sensor data synchronization

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/2183/45880

Stroke Classification Comparison with KNN through Standardization and Normalization Techniques

Autoren: Firmansyah, Muhammad Raihan Astuti, Yani Parti Muhammad Raihan Firmansyah et al.

Weitere Verfasser: Firmansyah, Muhammad Raihan Astuti, Yani Parti Muhammad Raihan Firmansyah et al.

Quelle: Advance Sustainable Science, Engineering and Technology; Vol 6, No 1 (2024): November-January; 02401012 ; 2715-4211

Schlagwörter: KNN, Z-Score Standardization, Min Max Normalization, Stroke Classification, Data Scaling

Dateibeschreibung: application/pdf

Relation: http://journal.upgris.ac.id/index.php/asset/article/view/17685/pdf; http://journal.upgris.ac.id/index.php/asset/article/view/17685

Verfügbarkeit: http://journal.upgris.ac.id/index.php/asset/article/view/17685
https://doi.org/10.26877/asset.v6i1.17685

Classification of posterior circulation stroke: a narrative review of terminology and history

Autoren: Prokopiv, Maria M. Fartushna, Olena Ye.

Quelle: INTERNATIONAL NEUROLOGICAL JOURNAL; Vol. 17 No. 5 (2021); 5-9
МЕЖДУНАРОДНЫЙ НЕВРОЛОГИЧЕСКИЙ ЖУРНАЛ; Том 17 № 5 (2021); 5-9
МІЖНАРОДНИЙ НЕВРОЛОГІЧНИЙ ЖУРНАЛ; Том 17 № 5 (2021); 5-9

Schlagwörter: вертебробазилярний інсульт, класифікація, історія, огляд, 03 medical and health sciences, 0302 clinical medicine, posterior circulation stroke, classification, history, review, 16. Peace & justice, 3. Good health

Dateibeschreibung: application/pdf

Zugangs-URL: http://inj.zaslavsky.com.ua/article/view/238517

Simulation-based Machine Learning Training for Brain Anomalies Localization at Microwaves

Autoren: Valeria Mariano Mario R. Casu Francesca Vipiana

Quelle: 2022 16th European Conference on Antennas and Propagation (EuCAP). :1-3

Schlagwörter: 03 medical and health sciences, Machine learning, brain stroke classification, multilayer perceptron, microwave imaging, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/11583/2964071
https://ieeexplore.ieee.org/document/9769504
https://doi.org/10.23919/EuCAP53622.2022.9769504

Comparative Analysis of Deep Learning Methods for Stroke Classification Using ResNet Architectures

Autoren: Yelken, E. Ceylan, M.

Schlagwörter: Deep Learning, Computed Tomography, Medical Image Analysis, ResNet Models, Stroke Classification

Relation: EAI/Springer Innovations in Communication and Computing; Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı; https://hdl.handle.net/20.500.13091/10987; 163; 176; Q3

Verfügbarkeit: https://hdl.handle.net/20.500.13091/10987
https://doi.org/10.1007/978-3-031-88999-8_13