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Transfer Learning-Based Classification Comparison of Stroke

Year 2022, , 192 - 201, 10.10.2022
https://doi.org/10.53070/bbd.1172807

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.

Thanks

Bu çalışma "6th International Artificial Intelligence and Data Processing Symposium"da bildiri olarak sunulmuştur.

References

  • Agarwal, V. (2020). Complete Architectural Details of all EfficientNet Models. [Cited Online]: https://towardsdatascience.com/complete-architectural-details-of-all-efficientnet-models-5fd5b736142
  • 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.
  • Ananda Kumar, S., & Mahesh, G. (2021). IoT in Smart Healthcare System. https://doi.org/10.1007/978-981-15-4112-4_1
  • 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
  • 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
  • Di Carlo, A. (2009). Human and economic burden of stroke. Age and Ageing, 38(1), 4–5. https://doi.org/10.1093/ageing/afn282
  • 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
  • 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
  • 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
  • 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 after endovascular treatment of patients with acute ischemic stroke. Computers in Biology and Medicine, 115, 103516. https://doi.org/10.1016/j.compbiomed.2019.103516
  • Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Arxiv. https://doi.org/10.48550/arxiv.1704.04861
  • Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016). Densely Connected Convolutional Networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 2261–2269. https://doi.org/10.48550/arxiv.1608.06993
  • Johnson, W., Onuma, O., Owolabi, M., & Sachdev, S. (2016). Stroke: A global response is needed. Bulletin of the World Health Organization, 94(9), 634A-635A. https://doi.org/10.2471/BLT.16.181636
  • Kim, J. K., Choo, Y. J., & Chang, M. C. (2021). Prediction of Motor Function in Stroke Patients Using Machine Learning Algorithm: Development of Practical Models. Journal of Stroke and Cerebrovascular Diseases, 30(8), 105856. https://doi.org/10.1016/j.jstrokecerebrovasdis.2021.105856
  • Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–15.
  • Kumar, S., Negi, A., Singh, J. N., & Verma, H. (2018). A deep learning for brain tumor mri images semantic segmentation using FCN. 2018 4th International Conference on Computing Communication and Automation, ICCCA 2018, February 2022. https://doi.org/10.1109/CCAA.2018.8777675
  • Kursad Poyraz, A., Dogan, S., Akbal, E., & Tuncer, T. (2022). Automated brain disease classification using exemplar deep features. Biomedical Signal Processing and Control, 73(January 2021), 103448. https://doi.org/10.1016/j.bspc.2021.103448
  • Lei, B., Liang, E., Yang, M., Yang, P., Zhou, F., Tan, E. L., Lei, Y., Liu, C. M., Wang, T., Xiao, X., & Wang, S. (2022). Predicting clinical scores for Alzheimer’s disease based on joint and deep learning. Expert Systems with Applications, 187(September 2021), 115966. https://doi.org/10.1016/j.eswa.2021.115966
  • Liu, J., Xu, H., Chen, Q., Zhang, T., Sheng, W., Huang, Q., Song, J., Huang, D., Lan, L., Li, Y., Chen, W., & Yang, Y. (2019). Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine. EBioMedicine, 43, 454–459. https://doi.org/10.1016/j.ebiom.2019.04.040
  • Liu, T., Fan, W., & Wu, C. (2019). A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset. Artificial Intelligence in Medicine, 101, 101723. https://doi.org/10.1016/j.artmed.2019.101723
  • Lu, D., Polomac, N., Gacheva, I., Hattingen, E., & Triesch, J. (2021). Human-expert-level brain tumor detection using deep learning with data distillation and augmentation. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2021-June, 3975–3979. https://doi.org/10.1109/ICASSP39728.2021.9415067
  • Merino, J. G. (2014). Clinical stroke challenges: A practical approach. Neurology: Clinical Practice, 4(5), 376–377. https://doi.org/10.1212/CPJ.0000000000000082
  • Muhammad Usman, S., Khalid, S., & Bashir, S. (2021). A deep learning based ensemble learning method for epileptic seizure prediction. Computers in Biology and Medicine, 136(July), 104710. https://doi.org/10.1016/j.compbiomed.2021.104710
  • Oksuz, I. (2021). Brain MRI artefact detection and correction using convolutional neural networks. Computer Methods and Programs in Biomedicine, 199, 105909. https://doi.org/10.1016/j.cmpb.2020.105909
  • Peng, H., Gong, W., Beckmann, C. F., Vedaldi, A., & Smith, S. M. (2021). Accurate brain age prediction with lightweight deep neural networks. Medical Image Analysis, 68, 101871. https://doi.org/10.1016/j.media.2020.101871
  • Savaş, S. (2022). Detecting the Stages of Alzheimer’s Disease with Pre-trained Deep Learning Architectures. Arabian Journal for Science and Engineering, 47(2), 2201–2218. https://doi.org/10.1007/s13369-021-06131-3
  • Savaş, S., Topaloğlu, N., Kazcı, Ö., & Koşar, P. N. (2019). Performance Comparison of Carotid Artery Intima Media Thickness Classification by Deep Learning Methods. International Congress on Human-Computer Interaction, Optimization and Robotic Applications Proceedings, 4(5), 125–131. https://doi.org/10.36287/setsci.4.5.025
  • Savaş, S., Topaloğlu, N., Kazcı, Ö., & Koşar, P. N. (2022). Comparison of Deep Learning Models in Carotid Artery Intima-Media Thickness Ultrasound Images: CAIMTUSNet. Bilişim Teknolojileri Dergisi, 15(1), 1–12.
  • Shankar, A., Khaing, H. K., Dandapat, S., & Barma, S. (2021). Analysis of epileptic seizures based on EEG using recurrence plot images and deep learning. Biomedical Signal Processing and Control, 69(May), 102854. https://doi.org/10.1016/j.bspc.2021.102854
  • Shoeibi, A., Khodatars, M., Ghassemi, N., Jafari, M., Moridian, P., Alizadehsani, R., Panahiazar, M., Khozeimeh, F., Zare, A., Hosseini-Nejad, H., Khosravi, A., Atiya, A. F., Aminshahidi, D., Hussain, S., Rouhani, M., Nahavandi, S., & Acharya, U. R. (2021). Epileptic seizures detection using deep learning techniques: A review. International Journal of Environmental Research and Public Health, 18(11). https://doi.org/10.3390/ijerph18115780
  • Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. https://doi.org/10.48550/arxiv.1409.1556
  • Sirsat, M. S., Fermé, E., & Câmara, J. (2020). Machine Learning for Brain Stroke: A Review. Journal of Stroke and Cerebrovascular Diseases, 29(10). https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105162
  • Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 2818–2826. https://doi.org/10.1109/CVPR.2016.308
  • Tan, M., & Le, Q. v. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 36th International Conference on Machine Learning, ICML 2019, 2019-June, 10691–10700. https://doi.org/10.48550/arxiv.1905.11946
  • Tanner, M. A., & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528–540. https://doi.org/10.1080/01621459.1987.10478458
  • Thornhill, R. E., Lum, C., Jaberi, A., Stefanski, P., Torres, C. H., Momoli, F., Petrcich, W., & Dowlatshahi, D. (2014). Can shape analysis differentiate free-floating internal carotid artery thrombus from atherosclerotic plaque in patients evaluated with CTA for stroke or transient ischemic attack? Academic Radiology, 21(3), 345–354. https://doi.org/10.1016/j.acra.2013.11.011
  • Vargas, J., Spiotta, A., & Chatterjee, A. R. (2019). Initial Experiences with Artificial Neural Networks in the Detection of Computed Tomography Perfusion Deficits. World Neurosurgery, 124, e10–e16. https://doi.org/10.1016/j.wneu.2018.10.084
  • Zhu, Y., & Newsam, S. (2018). DenseNet for dense flow. Proceedings - International Conference on Image Processing, ICIP, 2017-September, 790–794. https://doi.org/10.1109/ICIP.2017.8296389

Transfer Learning-Based Classification Comparison of Stroke

Year 2022, , 192 - 201, 10.10.2022
https://doi.org/10.53070/bbd.1172807

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.

References

  • Agarwal, V. (2020). Complete Architectural Details of all EfficientNet Models. [Cited Online]: https://towardsdatascience.com/complete-architectural-details-of-all-efficientnet-models-5fd5b736142
  • 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.
  • Ananda Kumar, S., & Mahesh, G. (2021). IoT in Smart Healthcare System. https://doi.org/10.1007/978-981-15-4112-4_1
  • 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
  • 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
  • Di Carlo, A. (2009). Human and economic burden of stroke. Age and Ageing, 38(1), 4–5. https://doi.org/10.1093/ageing/afn282
  • 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
  • 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
  • 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
  • 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 after endovascular treatment of patients with acute ischemic stroke. Computers in Biology and Medicine, 115, 103516. https://doi.org/10.1016/j.compbiomed.2019.103516
  • Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Arxiv. https://doi.org/10.48550/arxiv.1704.04861
  • Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016). Densely Connected Convolutional Networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 2261–2269. https://doi.org/10.48550/arxiv.1608.06993
  • Johnson, W., Onuma, O., Owolabi, M., & Sachdev, S. (2016). Stroke: A global response is needed. Bulletin of the World Health Organization, 94(9), 634A-635A. https://doi.org/10.2471/BLT.16.181636
  • Kim, J. K., Choo, Y. J., & Chang, M. C. (2021). Prediction of Motor Function in Stroke Patients Using Machine Learning Algorithm: Development of Practical Models. Journal of Stroke and Cerebrovascular Diseases, 30(8), 105856. https://doi.org/10.1016/j.jstrokecerebrovasdis.2021.105856
  • Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–15.
  • Kumar, S., Negi, A., Singh, J. N., & Verma, H. (2018). A deep learning for brain tumor mri images semantic segmentation using FCN. 2018 4th International Conference on Computing Communication and Automation, ICCCA 2018, February 2022. https://doi.org/10.1109/CCAA.2018.8777675
  • Kursad Poyraz, A., Dogan, S., Akbal, E., & Tuncer, T. (2022). Automated brain disease classification using exemplar deep features. Biomedical Signal Processing and Control, 73(January 2021), 103448. https://doi.org/10.1016/j.bspc.2021.103448
  • Lei, B., Liang, E., Yang, M., Yang, P., Zhou, F., Tan, E. L., Lei, Y., Liu, C. M., Wang, T., Xiao, X., & Wang, S. (2022). Predicting clinical scores for Alzheimer’s disease based on joint and deep learning. Expert Systems with Applications, 187(September 2021), 115966. https://doi.org/10.1016/j.eswa.2021.115966
  • Liu, J., Xu, H., Chen, Q., Zhang, T., Sheng, W., Huang, Q., Song, J., Huang, D., Lan, L., Li, Y., Chen, W., & Yang, Y. (2019). Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine. EBioMedicine, 43, 454–459. https://doi.org/10.1016/j.ebiom.2019.04.040
  • Liu, T., Fan, W., & Wu, C. (2019). A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset. Artificial Intelligence in Medicine, 101, 101723. https://doi.org/10.1016/j.artmed.2019.101723
  • Lu, D., Polomac, N., Gacheva, I., Hattingen, E., & Triesch, J. (2021). Human-expert-level brain tumor detection using deep learning with data distillation and augmentation. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2021-June, 3975–3979. https://doi.org/10.1109/ICASSP39728.2021.9415067
  • Merino, J. G. (2014). Clinical stroke challenges: A practical approach. Neurology: Clinical Practice, 4(5), 376–377. https://doi.org/10.1212/CPJ.0000000000000082
  • Muhammad Usman, S., Khalid, S., & Bashir, S. (2021). A deep learning based ensemble learning method for epileptic seizure prediction. Computers in Biology and Medicine, 136(July), 104710. https://doi.org/10.1016/j.compbiomed.2021.104710
  • Oksuz, I. (2021). Brain MRI artefact detection and correction using convolutional neural networks. Computer Methods and Programs in Biomedicine, 199, 105909. https://doi.org/10.1016/j.cmpb.2020.105909
  • Peng, H., Gong, W., Beckmann, C. F., Vedaldi, A., & Smith, S. M. (2021). Accurate brain age prediction with lightweight deep neural networks. Medical Image Analysis, 68, 101871. https://doi.org/10.1016/j.media.2020.101871
  • Savaş, S. (2022). Detecting the Stages of Alzheimer’s Disease with Pre-trained Deep Learning Architectures. Arabian Journal for Science and Engineering, 47(2), 2201–2218. https://doi.org/10.1007/s13369-021-06131-3
  • Savaş, S., Topaloğlu, N., Kazcı, Ö., & Koşar, P. N. (2019). Performance Comparison of Carotid Artery Intima Media Thickness Classification by Deep Learning Methods. International Congress on Human-Computer Interaction, Optimization and Robotic Applications Proceedings, 4(5), 125–131. https://doi.org/10.36287/setsci.4.5.025
  • Savaş, S., Topaloğlu, N., Kazcı, Ö., & Koşar, P. N. (2022). Comparison of Deep Learning Models in Carotid Artery Intima-Media Thickness Ultrasound Images: CAIMTUSNet. Bilişim Teknolojileri Dergisi, 15(1), 1–12.
  • Shankar, A., Khaing, H. K., Dandapat, S., & Barma, S. (2021). Analysis of epileptic seizures based on EEG using recurrence plot images and deep learning. Biomedical Signal Processing and Control, 69(May), 102854. https://doi.org/10.1016/j.bspc.2021.102854
  • Shoeibi, A., Khodatars, M., Ghassemi, N., Jafari, M., Moridian, P., Alizadehsani, R., Panahiazar, M., Khozeimeh, F., Zare, A., Hosseini-Nejad, H., Khosravi, A., Atiya, A. F., Aminshahidi, D., Hussain, S., Rouhani, M., Nahavandi, S., & Acharya, U. R. (2021). Epileptic seizures detection using deep learning techniques: A review. International Journal of Environmental Research and Public Health, 18(11). https://doi.org/10.3390/ijerph18115780
  • Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. https://doi.org/10.48550/arxiv.1409.1556
  • Sirsat, M. S., Fermé, E., & Câmara, J. (2020). Machine Learning for Brain Stroke: A Review. Journal of Stroke and Cerebrovascular Diseases, 29(10). https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105162
  • Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 2818–2826. https://doi.org/10.1109/CVPR.2016.308
  • Tan, M., & Le, Q. v. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 36th International Conference on Machine Learning, ICML 2019, 2019-June, 10691–10700. https://doi.org/10.48550/arxiv.1905.11946
  • Tanner, M. A., & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528–540. https://doi.org/10.1080/01621459.1987.10478458
  • Thornhill, R. E., Lum, C., Jaberi, A., Stefanski, P., Torres, C. H., Momoli, F., Petrcich, W., & Dowlatshahi, D. (2014). Can shape analysis differentiate free-floating internal carotid artery thrombus from atherosclerotic plaque in patients evaluated with CTA for stroke or transient ischemic attack? Academic Radiology, 21(3), 345–354. https://doi.org/10.1016/j.acra.2013.11.011
  • Vargas, J., Spiotta, A., & Chatterjee, A. R. (2019). Initial Experiences with Artificial Neural Networks in the Detection of Computed Tomography Perfusion Deficits. World Neurosurgery, 124, e10–e16. https://doi.org/10.1016/j.wneu.2018.10.084
  • Zhu, Y., & Newsam, S. (2018). DenseNet for dense flow. Proceedings - International Conference on Image Processing, ICIP, 2017-September, 790–794. https://doi.org/10.1109/ICIP.2017.8296389
There are 38 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section PAPERS
Authors

Rusul Ali Jabbar Alhatemi This is me 0000-0002-0102-2194

Serkan Savaş 0000-0003-3440-6271

Publication Date October 10, 2022
Submission Date September 9, 2022
Acceptance Date September 16, 2022
Published in Issue Year 2022

Cite

APA Alhatemi, R. A. J., & Savaş, S. (2022). Transfer Learning-Based Classification Comparison of Stroke. Computer Science, IDAP-2022 : International Artificial Intelligence and Data Processing Symposium, 192-201. https://doi.org/10.53070/bbd.1172807

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