Pre Diagnosing the Stroke Using Deep Learning
DOI:
https://doi.org/10.29304/jqcm.2022.14.3.984Keywords:
The Stroke, CNN, EfficientNetB0, Transfer LearningAbstract
Stroke is a long-term disability that affects people all around the world. A stroke deprives the brain of oxygen and nutrients, causing brain cells to stop working or die. Radiologists classify them using Magnetic Resonance Imaging(MRI) of the brain for people with stroke. We've presented a solution based on Deep Learning Algorithms such as Convolution Neural Networks (CNN) and Transfer Learning (TL). We divided MRI into two categories in this work (stroke and normal). In this paper, a 4-layer CNN is used from beginning to end with a modification of the EfficientNetB0 network by adding two layers to make the extracted features more diverse. CNN and pre-trained EfficientNetB0 were used to train our architecture. The EfficientNetB0 achieves an accuracy of 99.6%, while CNN achieves an accuracy of 99.2%. The newly created EfficientNetB0 could be a highly important decision-making tool in stroke research and brain diagnostic testing. As a physician's assistant, the proposed method works.
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References
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[3] M. T. Altemimi and A. R. Hashim, "Acute stroke in diabetes mellitus: a prospective observational study evaluating the course and short-term outcome in basrah, southern Iraq," Cureus, vol. 11, no. 10, 2019.
[4] M. C. Mabray and S. Cha, "Advanced MR imaging techniques in daily practice," Neuroimaging Clinics, vol. 26, no. 4, pp. 647-666, 2016.
[5] Y. Bengio and H. Lee, "Editorial introduction to the neural networks special issue on deep learning of representations," Neural Networks, vol. 64, no. C, pp. 1-3, 2015.
[6] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," nature, vol. 521, no. 7553, pp. 436-444, 2015.
[7] Z. Mousavi, T. Y. Rezaii, S. Sheykhivand, A. Farzamnia, and S. Razavi, "Deep convolutional neural network for classification of sleep stages from single-channel EEG signals," Journal of neuroscience methods, vol. 324, p. 108312, 2019.
[8] E. Başaran, Z. Cömert, and Y. Çelik, "Convolutional neural network approach for automatic tympanic membrane detection and classification," Biomedical Signal Processing and Control, vol. 56, p. 101734, 2020.
[9] H.-I. Suk, S.-W. Lee, D. Shen, and A. s. D. N. Initiative, "Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis," NeuroImage, vol. 101, pp. 569-582, 2014.
[10] A. Feizi, "Convolutional gating network for object tracking," International Journal of Engineering, vol. 32, no. 7, pp. 931-939, 2019.
[11] Z. A. Khalaf, S. S. Hammadi, A. K. Mousa, H. M. Ali, H. R. Alnajar, and R. H. Mohsin, "Coronavirus disease 2019 detection using deep features learning," International Journal of Electrical & Computer Engineering (2088-8708), vol. 12, no. 4, 2022.
[12] N. M. A.-M. M. Al and R. S. Khudeyer, "ResNet-34/DR: A Residual Convolutional Neural Network for the Diagnosis of Diabetic Retinopathy," Informatica, vol. 45, no. 7, 2021.
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[16] N. Takahashi et al., "Computerized identification of early ischemic changes in acute stroke in noncontrast CT using deep learning," in Medical Imaging 2019: Computer-Aided Diagnosis, 2019, vol. 10950: SPIE, pp. 826-831.
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[18] W. Du, Y. He, Y. Li, and Z. Wu, "Brain tumor diagnosis using EfficientNet," in Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, vol. 12079: SPIE, pp. 18-25.
[19] M. Al-Smadi, M. Hammad, Q. B. Baker, and S. a. A. Al-Zboon, "A transfer learning with deep neural network approach for diabetic retinopathy classification," International Journal of Electrical & Computer Engineering (2088-8708), vol. 11, no. 4, 2021.
[20] R. Poojary, R. Raina, and A. K. Mondal, "Effect of data-augmentation on fine-tuned CNN model performance," IAES International Journal of Artificial Intelligence, vol. 10, no. 1, p. 84, 2021.
[21] T. Ateeq et al., "Ensemble-classifiers-assisted detection of cerebral microbleeds in brain MRI," Computers & Electrical Engineering, vol. 69, pp. 768-781, 2018.
[22] S. B. Jadhav, "Convolutional neural networks for leaf image-based plant disease classification," IAES International Journal of Artificial Intelligence, vol. 8, no. 4, p. 328, 2019.
[23] A. A. Alani and G. Cosma, "ArSL-CNN: a convolutional neural network for Arabic sign language gesture recognition," 2021.
[24] S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345-1359, 2009.
[25] H. Pratt, F. Coenen, S. P. Harding, D. M. Broadbent, and Y. Zheng, "Feature visualisation of classification of diabetic retinopathy using a convolutional neural network," in CEUR Workshop Proceedings, 2019, vol. 2429, pp. 23-29.
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[27] O. Russakovsky et al., "Imagenet large scale visual recognition challenge," International journal of computer vision, vol. 115, no. 3, pp. 211-252, 2015.
[28] J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132-7141.
[29] Y. Huang et al., "Gpipe: Efficient training of giant neural networks using pipeline parallelism," Advances in neural information processing systems, vol. 32, 2019.
[30] M. Grandini, E. Bagli, and G. Visani, "Metrics for multi-class classification: an overview," arXiv preprint arXiv:2008.05756, 2020.
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Published
2022-08-12
How to Cite
Atshan, M. S., & Nemer, Z. N. (2022). Pre Diagnosing the Stroke Using Deep Learning. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(3), Comp Page 33–43. https://doi.org/10.29304/jqcm.2022.14.3.984
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Computer Articles
