Open Access
Issue
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
Article Number 01007
Number of page(s) 8
DOI https://doi.org/10.1051/itmconf/20257901007
Published online 08 October 2025
  1. A. Tursynova, B. Omarov, N. Tukenova, I. Salgozha, O. Khaaval, R. Ramazanov, B. Ospanov, Deep learning-enabled brain stroke classification on computed tomography images. Comput. Mater. Continua. 75, 1431–1446 (2023). https://doi.org/10.32604/cmc.2023.034400 [Google Scholar]
  2. C.H. Patel, D. Undaviya, H. Dave, S. Degadwala, D. Vyas, EfficientNetB0 for brain stroke classification on computed tomography scan, In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), IEEE, Salem, India, June 08 (2023), 713–718 [Google Scholar]
  3. A.A. Eshmawi, M. Khayyat, A.D. Algarni, I. Hilali-Jaghdam, An ensemble of deep learning enabled brain stroke classification model in magnetic resonance images. J. Healthc. Eng. 2022, 1–11 (2022). https://doi.org/10.1155/2022/7815434 [CrossRef] [Google Scholar]
  4. A. Gautam, B. Raman, Towards effective classification of brain hemorrhagic and ischemic stroke using CNN. Biomed. Signal Process. Control. 63, 102178 (2021). https://doi.org/10.1016/j.bspc.2020.102178 [Google Scholar]
  5. S.S. Mousavi, M.S. Majedi, A quantitative microwave imaging approach for brain stroke classification based on the generalized Tikhonov regularization. IEEE Access 11, 73370–73376 (2023). 10.1109/ACCESS.2023.3295692 [Google Scholar]
  6. G. Sailasya, G.L. Kumari, Analyzing the performance of stroke prediction using ML classification algorithms. Int. J. Adv. Comput. Sci. Appl. 12, 539–545 (2021). 10.14569/IJACSA.2021.0120662 [Google Scholar]
  7. S.K. Thiyagarajan, K. Murugan, A systematic review on techniques adapted for segmentation and classification of ischemic stroke lesions from brain MR images. Wireless Pers. Commun. 118, 12251244 (2021). https://doi.org/10.1007/s11277-021-08069-z [Google Scholar]
  8. S. Rahman, M. Hasan, A.K. Sarkar, Prediction of brain stroke using machine learning algorithms and deep neural network techniques. Eur. J. Electr. Eng. Comput. Sci. 7, 23–30 (2023). https://doi.org/10.24018/ejece.2023.7.1.751 [Google Scholar]
  9. Vavoulis, P., Figueiredo, A., Vourvopoulos, A review of online classification performance in motor imagery-based brain–computer interfaces for stroke neurorehabilitation. Signals 4, 73–86 (2023). https://doi.org/10.3390/signals4010004 [Google Scholar]
  10. J.N. Fernandes, V.E. Cardoso, A. Comesaña-Campos, A. Pinheira, Comprehensive review: Machine and deep learning in brain stroke diagnosis. Sens. 24, 4355 (2024). https://doi.org/10.3390/s24134355 [Google Scholar]
  11. M. Deepa, M. Murugappan, M.G. Sumithra, M. Mahmud, M.S. Al-Rakhami, Pattern descriptors orientation and MAP firefly algorithm-based brain pathology classification using hybridized machine learning algorithm. IEEE Access 10, 3848–3863 (2021). 10.1109/access.2021.3100549 [Google Scholar]
  12. A.K. Sharma, A. Nandal, A. Dhaka, K. Polat, R. Alwadie, F. Alenezi, A. Alhudhaif, HOG transformation-based feature extraction framework in modified Resnet50 model for brain tumor detection. Biomed. Signal Process Control 84, 104737 (2023). https://doi.org/10.1016/j.bspc.2023.104737 [Google Scholar]
  13. S. Gavkare, R. Umbare, K. Shinde, P. Patange, Y. Katmore, Characterizing and classifying brain tumors via HOG features, In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0, IEEE, Raigarh, India, September 30 (2024), 1–6 [Google Scholar]
  14. A. Taha, J. Bobi, R. Dammers, R.M. Dijkhuizen, A.Y. Dreyer, A.C. Van Es, F. Ferrara, M.J. Gounis, B. Nitzsche, S. Platt, M.H. Stoffel, Comparison of large animal models for acute ischemic stroke: which model to use?. Stroke 53, 1411–1422 (2022). https://doi.org/10.1161/STROKEAHA.121.036050 [Google Scholar]
  15. T. Pokorny, J. Vrba, O. Fiser, D. Vrba, T. Drizdal, M. Novak, L. Tosi, A. Polo, M. Salucci, On the role of training data for SVM-based microwave brain stroke detection and classification. Sens. 23, 2031 (2023). https://doi.org/10.3390/s23042031 [Google Scholar]
  16. V.S. Telu, V. Padimi, D.D. Ningombam, Optimizing predictions of brain stroke using machine learning. J. Neutrosophic Fuzzy Syst. 2, 31–43 (2022). https://doi.org/10.54216/JNFS.020203 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.