nach oben

Erschienen in:

2025 | OriginalPaper | Buchkapitel

Federated Deep Learning Models for Stroke Prediction

verfasst von : Asma Mansour, Olfa Besbes, Takoua Abdellatif

Erschienen in: Web Information Systems Engineering – WISE 2024

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Stroke is a life-threatening medical condition caused by an inadequate blood supply to the brain. According to the World Health Organization (WHO), stroke is a leading cause of death and disability worldwide. After a stroke, the affected brain areas fail to function normally, making early detection of warning signs crucial for effective treatment and reducing disease severity. Various Machine Learning (ML) and Deep Learning (DL) models have been developed to predict stroke occurrence. This research highlights the effectiveness of Federated Learning (FL), a decentralized training approach that bolsters privacy while preserving model performance. Our models outperform traditional ML and DL methods, achieving an accuracy of 98%. Evaluations using metrics such as accuracy, precision, recall, and F1 score confirm the robustness and generalizability of our approach.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Unsupervised and Dynamic Dendrogram-Based Visualization of Medical Data

https://github.com/google-parfait/tensorflow-federated.

https://flower.ai/.

RGPD: https://datalegaldrive.com/donnees-sante rgpd

McMahan, H.B., et al.: Communication-efficient learning of deep networks from decentralized data. In: International Conference on Artificial Intelligence and Statistics (2016)

Persson, M., et al.: Microwave-based stroke diagnosis making global prehospital thrombolytic treatment possible. IEEE Trans. Biomed. Eng. 61, 11 (2014)CrossRef

Salucci, M., Polo, A., Vrba, J.: Stratégie d’apprentissage par exemples en plusieurs étapes pour l’inversion des données de diffusion par micro-ondes en temps réel sur les accidents vasculaires cérébraux. Electronique (2021)

Lin, C.H., Hsu, K.C., Johnson, K.R., Luby, M., Fann, Y.C.: Applying density-based outlier identifications using multiple datasets for validation of stroke clinical outcomes. Int. J. Med. Inform. 132, 103988 (2019)

Liu, T., Fan, W., Wu, C.: A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset. Artificial Intell. Med. 101, 101723 (2019)

Garg, R., Oh, E., Naidech, A., Kording, K., Prabhakaran, S.: Automating ischemic stroke subtype classification using machine learning and natural language processing. J. Stroke Cerebrovascular Diseases 28(7), 2045–2051 (2019)

Sabut, S., Subudhi, A., Dash, M.: Automated segmentation and classification of brain stroke using expectation maximization and random forest classifier. Biocybernetics Biomedical Eng. (2019)

Ortiz-Ramón, R., et al.: Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images. Comput. Med. Imaging Graph. 74, 12–24 (2019)CrossRef

10.

Nurhayati, O., Windasari, I.P.: Stroke identification system on the mobile based CT scan image. In: 2015 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), pp. 113–116 (2015)

11.

Guoqing, W., et al.: Early identification of ischemic stroke in noncontrast computed tomography. Biomed. Signal Process. Control 52, 41–52 (2019)CrossRef

12.

DA, Rizki, Klasifikasi Pendarahan Otak Menggunakan Extreme Learning Machine. Universitas Sumatera Utara (2017)

13.

Chawla, M., Sharma, S., Sivaswamy, J., Kishore, L.: A method for automatic detection and classification of stroke from brain CT images. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2009)

14.

Marbun, J.T., Seniman1, Andayani, U.: Classification of stroke disease using convolutional neural network. J. Phys. Conf. Ser. 978(1), 012092 (2018)

15.

Sheller, M.J., Reina, G.A., Edwards, B., Martin, J., Bakas, S.: Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 92–104. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11723-8_9CrossRef

16.

Ce, J., et al.: Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention (2020)

17.

Li, W., et al.: Privacy-preserving federated brain tumour segmentation. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 133–141. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_16CrossRef

18.

Yu, T.T.L., et al.: Collaborative Diabetic Retinopathy Severity Classification of Optical Coherence Tomography Data through Federated Learning. Invest. Ophthalmol. Vis. Sci 62(8), 1029–1029 (2021)

19.

Lo, J., et al.: Federated learning for microvasculature segmentation and diabetic retinopathy classification of OCT data. Ophthalmol. Sci. 1(4), 100069 (2021)

20.

Nguyen, T.X., et al.: Federated learning in ocular imaging: current progress and future direction. Diagnostics 12(11), 2835 (2022)

21.

Elhanashi, A., Dini, P., Saponara, S., Zheng, Q.: TeleStroke: real-time stroke detection with federated learning and Yolov8 on edge devices. J. Real-Time Image Process. 21(4), 121 (2024)

22.

Balázs B., Allaart, C.G., van Halteren, A.: Predicting stroke outcome: a case for multimodal deep learning methods with tabular and CT Perfusion data. J. Artif. Intell. Med. 147, 102719 (2024)

23.

Öman, O., Mäkelä, T., Salli, E. et al.: 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke. Eur. Radiol. Exp. 3, 1–11 (2019)

24.

Laney, D.: 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group (2001)

25.

Zunair, H.: 3D image classification from CT scans. Computer Vision (23 Sep 2020)

26.

Nielsen, A., Hansen, M.B., Tietze, A., Mouridsen, K.: Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke 49(6), 1394–1401 (2018)

27.

Wang, H.L., et al.: Automatic machine-learning-based outcome prediction in patients with primary intracerebral hemorrhage. Front. Neurol. 10, 910 (2019)

Titel: Federated Deep Learning Models for Stroke Prediction
verfasst von: Asma Mansour
Olfa Besbes
Takoua Abdellatif
Verlag: Springer Nature Singapore
Buch: Web Information Systems Engineering – WISE 2024
Print ISBN: 978-981-9605-72-9

Electronic ISBN: 978-981-9605-73-6

Copyright-Jahr: 2025
DOI: https://doi.org/10.1007/978-981-96-0573-6_32

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"