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Gautam A, Raman B. Towards effective classification of brain hemorrhagic and ischemic stroke using CNN. Biomedical Signal Processing and Control. 2021;63:102178. https://doi.org/10.1016/j.bspc.2020.102178Search in Google Scholar
Jayachitra S, Prasanth A. Multi-Feature Analysis for Automated Brain Stroke Classification Using Weighted Gaussian Naïve Bayes Classifier. J CIRCUIT SYST COMP. 2021;30(10):2150178. https://doi.org/10.1142/S0218126621501784Search in Google Scholar
Karadima O, Rahman M, Sotiriou I, et al. Experimental Validation of Microwave Tomography with the DBIM-TwIST Algorithm for Brain Stroke Detection and Classification. Sensors. 2020;20(3):840. https://doi.org/10.3390/s20030840Search in Google Scholar
Öman O, Mäkelä T, Salli E, Savolainen S, Kangasniemi M. 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke. Eur Radiol Exp. 2019;3(1). https://doi.org/10.1186/s41747-019-0085-6Search in Google Scholar
Abramova V, Clèrigues A, Quiles A, et al. Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks. Computerized Medical Imaging and Graphics. 2021;90:101908. https://doi.org/10.1016/j.compmedimag.2021.101908Search in Google Scholar
Mansour RF, Aljehane NO. An optimal segmentation with deep learning based inception network model for intracranial hemorrhage diagnosis. Neural Comput & Applic. 2021;33(20):13831-13843. https://doi.org/10.1007/s00521-021-06020-8Search in Google Scholar
Inkeaw P, Angkurawaranon S, Khumrin P, et al. Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model. Computers in Biology and Medicine. 2022;146:105530. https://doi.org/10.1016/j.compbiomed.2022.105530Search in Google Scholar
Arab A, Chinda B, Medvedev G, et al. A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT. Sci Rep. 2020;10(1). https://doi.org/10.1038/s41598-020-76459-7Search in Google Scholar
Chen YT, Chen YL, Chen YY, et al. Deep Learning–Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke. Diagnostics. 2022;12(4):807. https://doi.org/10.3390/diagnostics12040807Search in Google Scholar
URAL AB. Computer-Aided Deep Learning Based Assessment of Stroke From Brain Radiological CT Images. European Journal of Science and Technology. 2022;34:42-52. https://doi.org/10.31590/ejosat.1063356Search in Google Scholar
Peng SJ, Chen YW, Yang JY, Wang KW, Tsai JZ. Automated Cerebral Infarct Detection on Computed Tomography Images Based on Deep Learning. Biomedicines. 2022;10(1):122. https://doi.org/10.3390/biomedicines10010122Search in Google Scholar
Sarmento RM, Vasconcelos FFX, Filho PPR, de Albuquerque VHC. An IoT platform for the analysis of brain CT images based on Parzen analysis. Future Generation Computer Systems. 2020;105:135-147. https://doi.org/10.1016/j.future.2019.11.033Search in Google Scholar
Omarov B, Tursynova A, Postolache O, et al. Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images. Computers, Materials & Continua. 2022;71(3):4701-4717. https://doi.org/10.32604/cmc.2022.020998Search in Google Scholar
Tursynova A, Omarov B, Sakhipov A, Tukenova N. Brain Stroke Lesion Segmentation Using Computed Tomography Images based on Modified U-Net Model with ResNet Blocks. Int J Onl Eng. 2022;18(13):97-112. https://doi.org/10.3991/ijoe.v18i13.32881Search in Google Scholar
Kumar A, Ghosal P, Kundu SS, Mukherjee A, Nandi D. A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images. Computer Methods and Programs in Biomedicine. 2022;226:107157. https://doi.org/10.1016/j.cmpb.2022.107157Search in Google Scholar
Surya S, Yamini B, Rajendran T, Narayanan KE. A Comprehensive Method for Identification of Stroke Using Deep Learning. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2021;12(7):647-652.Search in Google Scholar
Kuo W, Hӓne C, Mukherjee P, Malik J, Yuh EL. Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning. Proc Natl Acad Sci USA. 2019;116(45):22737-22745. https://doi.org/10.1073/pnas.1908021116Search in Google Scholar
Qiu W, Kuang H, Teleg E, et al. Machine Learning for Detecting Early Infarction in Acute Stroke with Non–Contrast-enhanced CT. Radiology. 2020;294(3):638-644. https://doi.org/10.1148/radiol.2020191193Search in Google Scholar
Rebouças Filho PP, Sarmento RM, Holanda GB, de Alencar Lima D. New approach to detect and classify stroke in skull CT images via analysis of brain tissue densities. Computer Methods and Programs in Biomedicine. 2017;148:27-43. https://doi.org/10.1016/j.cmpb.2017.06.011Search in Google Scholar
Neethi AS, Niyas S, Kannath SK, Mathew J, Anzar AM, Rajan J. Stroke classification from computed tomography scans using 3D convolutional neural network. Biomedical Signal Processing and Control. 2022;76:103720. https://doi.org/10.1016/j.bspc.2022.103720Search in Google Scholar
Dourado Jr CMJM, da Silva SPP, da Nóbrega RVM, da S. Barros AC, Filho PPR, de Albuquerque VHC. Deep learning IoT system for online stroke detection in skull computed tomography images. Computer Networks. 2019;152:25-39. https://doi.org/10.1016/j.comnet.2019.01.019Search in Google Scholar
Li L, Wei M, Liu B, et al. Deep Learning for Hemorrhagic Lesion Detection and Segmentation on Brain CT Images. IEEE J Biomed Health Inform. 2021;25(5):1646-1659. https://doi.org/10.1109/JBHI.2020.3028243Search in Google Scholar
Mushtaq MF, Shahroz M, Aseere AM, et al. BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan. IEEE Access. 2021;9:113901-113916. https://doi.org/10.1109/ACCESS.2021.3102740Search in Google Scholar
Jnawali K, Arbabshirani MR, Rao N, Patel AA. Deep 3D convolution neural network for CT brain hemorrhage classification. In Medical Imaging 2018: Computer-Aided Diagnosis (Vol. 10575, pp. 307-313). SPIE. https://doi.org/10.1117/12.2293725Search in Google Scholar
Kaya B, Önal M. A CNN transfer learning‐based approach for segmentation and classification of brain stroke from noncontrast CT images. Int J Imaging Syst Tech. 2023;33(4):1335-1352. https://doi.org/10.1002/ima.22864Search in Google Scholar
Raghavendra U, Pham TH, Gudigar A, et al. Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images. Complex Intell Syst. 2021;7(2):929-940. https://doi.org/10.1007/s40747-020-00257-xSearch in Google Scholar
Ozaltin O, Coskun O, Yeniay O, Subasi A. A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet. Bioengineering. 2022;9(12):783. https://doi.org/10.3390/bioengineering9120783Search in Google Scholar
Xu Y, Holanda G, Souza LFabricio de F, et al. Deep Learning-Enhanced Internet of Medical Things to Analyze Brain CT Scans of Hemorrhagic Stroke Patients: A New Approach. IEEE Sensors J. 2021;21(22):24941-24951. https://doi.org/10.1109/JSEN.2020.3032897Search in Google Scholar
Yalçın S, Vural H. Brain stroke classification and segmentation using encoder-decoder based deep convolutional neural networks. Computers in Biology and Medicine. 2022;149:105941. https://doi.org/10.1016/j.compbiomed.2022.105941Search in Google Scholar