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Journals / Bilgisayar Bilimleri / 2022 / Cilt: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium

Transfer Learning-Based Classification Comparison of Stroke

Rusul Ali Jabbar ALHATEMİ
Serkan SAVAŞ
Pages
192–201
Publication Date
2022-10-10
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The overall accuracy of the best model is 98.8% and the same value for precision, recall, and f1-score using the EfficientNetB2 model for transfer learning.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:One type of brain disease that significantly harms people's lives and health is stroke.
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Key Findings & Data:Therefore, in thus study, several deep neural network models, including DenseNet121, ResNet50, Xception, MobileNet, VGG16, and EfficientNetB2 are proposed for transfer learning to classify MR images into two categories (stroke and non-stroke) in order to study the characteristics of the stroke lesions and achieve full intelligent automatic detection.
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Conclusion & Impact:The overall accuracy of the best model is 98.8% and the same value for precision, recall, and f1-score using the EfficientNetB2 model for transfer learning.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Cerebrovascular · stroke · brain MR classification

Abstract

One type of brain disease that significantly harms people's lives and health is stroke. The diagnosis and management of strokes both heavily rely on the quantitative analysis of brain Magnetic Resonance (MR) images. The early diagnosis process is of great importance for the prevention of stroke cases. Stroke prediction is made possible by deep neural networks with the capacity for enormous data learning. Therefore, in thus study, several deep neural network models, including DenseNet121, ResNet50, Xception, MobileNet, VGG16, and EfficientNetB2 are proposed for transfer learning to classify MR images into two categories (stroke and non-stroke) in order to study the characteristics of the stroke lesions and achieve full intelligent automatic detection. The study dataset comprises of 1901 training images, 475 validation images, and 250 testing images. On the training and validation sets, data augmentation was used to increase the number of images to improve the models’ learning. The experimental results outperform all the state of arts that were used the same dataset. The overall accuracy of the best model is 98.8% and the same value for precision, recall, and f1-score using the EfficientNetB2 model for transfer learning.

Özet

One type of brain disease that significantly harms people's lives and health is stroke. The diagnosis and management of strokes both heavily rely on the quantitative analysis of brain Magnetic Resonance (MR) images. The early diagnosis process is of great importance for the prevention of stroke cases. Stroke prediction is made possible by deep neural networks with the capacity for enormous data learning. Therefore, in thus study, several deep neural network models, including DenseNet121, ResNet50, Xception, MobileNet, VGG16, and EfficientNetB2 are proposed for transfer learning to classify MR images into two categories (stroke and non-stroke) in order to study the characteristics of the stroke lesions and achieve full intelligent automatic detection. The study dataset comprises of 1901 training images, 475 validation images, and 250 testing images. On the training and validation sets, data augmentation was used to increase the number of images to improve the models’ learning. The experimental results outperform all the state of arts that were used the same dataset. The overall accuracy of the best model is 98.8% and the same value for precision, recall, and f1-score using the EfficientNetB2 model for transfer learning.

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Cite This Article

ALHATEMİ, R. A. J., SAVAŞ, S. (2022). Transfer Learning-Based Classification Comparison of Stroke. Bilgisayar Bilimleri, Cilt: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium, 192–201. https://ajindex.com/articles/743125

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References (10)

  1. Agarwal, V. (2020). Complete Architectural Details of all EfficientNet Models. [Cited Online]: https://towardsdatascience.com/complete-architectural-details-of-all-efficientnet-models-5fd5b736142
  2. Almeida, Y.; Sirsat, M.; Bermúdez i Badia, S. and Fermé, E. (2020). AI-Rehab: A Framework for AI Driven Neurorehabilitation Training - The Profiling Challenge. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Cognitive Health IT, ISBN 978-989-758-398-8, 845–853.
  3. Ananda Kumar, S., & Mahesh, G. (2021). IoT in Smart Healthcare System. https://doi.org/10.1007/978-981-15-4112-4_1
  4. Bacchi, S., Zerner, T., Oakden-Rayner, L., Kleinig, T., Patel, S., & Jannes, J. (2020). Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes: A Pilot Study. Academic Radiology, 27(2), e19–e23. https://doi.org/10.1016/j.acra.2019.03.015
  5. Chollet, F. (2016). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 1800–1807. https://doi.org/10.48550/arxiv.1610.02357
  6. Di Carlo, A. (2009). Human and economic burden of stroke. Age and Ageing, 38(1), 4–5. https://doi.org/10.1093/ageing/afn282
  7. Ge, Y., Wang, Q., Wang, L., Wu, H., Peng, C., Wang, J., Xu, Y., Xiong, G., Zhang, Y., & Yi, Y. (2019). Predicting post-stroke pneumonia using deep neural network approaches. International Journal of Medical Informatics, 132(November 2018), 103986. https://doi.org/10.1016/j.ijmedinf.2019.103986
  8. Giacalone, M., Rasti, P., Debs, N., Frindel, C., Cho, T. H., Grenier, E., & Rousseau, D. (2018). Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke. Medical Image Analysis, 50, 117–126. https://doi.org/10.1016/j.media.2018.08.008
  9. He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 770–778. https://doi.org/10.48550/arxiv.1512.03385
  10. Hilbert, A., Ramos, L. A., van Os, H. J. A., Olabarriaga, S. D., Tolhuisen, M. L., Wermer, M. J. H., Barros, R. S., van der Schaaf, I., Dippel, D., Roos, Y. B. W. E. M., van Zwam, W. H., Yoo, A. J., Emmer, B. J., Lycklama à Nijeholt, G. J., Zwinderman, A. H., Strijkers, G. J., Majoie, C. B. L. M., & Marquering, H. A. (2019). Data-efficient deep learning of radiological image data for outcome prediction a