ARCHIVES
Original Article
Innovation in Stroke Identification Using Machine Learning Based Approach Using Neuro images
Deepika M1
Keerthiraj R2
Mithun Kumar S3
Kumaraswamy HJ4
1 Assistant Professor, Department of Computer Science and Engineering Rajarajeswari College of Engineering Bangalore, Karnataka, India. 2 3 4 Department of Computer Science and Engineering Rajarajeswari College of Engineering Bangalore, Karnataka, India.
Published Online: November-December 2025
Pages: 100-104
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250606015
References
1. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1251–1258.
2. Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations.
3. Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. 2017. Inception- v4, Inception-ResNet and the impact of residual connections on learning. AAAI Conference on Artificial Intelligence
4. Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. IEEE CVPR, 8697–8710.
5. Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. IEEE CVPR, 6848–6856.
6. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory.
7. Graves, A., & Schmiduber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5), 602–610.
8. Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. IEEE CVPR, 8697–8710.
9. Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. IEEE CVPR, 6848–6856.
10. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
11. Graves, A., & Schmiduber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5), 602–610.
12. Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–72.
13. Bhattacharya, S., Ghosh, S., & Chakraborty, S. (2020). Brain hemorrhage detection from CT images using deep neural networks. International Journal of Imaging Systems and Technology, 30(3), 635– 645.
14. Liang, X., Chen, T., Chen, Y., & Li, Y. (2021). Automatic ischemic stroke detection using CNN features and optimized classifiers. Biomedical Signal Processing and Control, 68, 102–118.
2. Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations.
3. Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. 2017. Inception- v4, Inception-ResNet and the impact of residual connections on learning. AAAI Conference on Artificial Intelligence
4. Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. IEEE CVPR, 8697–8710.
5. Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. IEEE CVPR, 6848–6856.
6. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory.
7. Graves, A., & Schmiduber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5), 602–610.
8. Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. IEEE CVPR, 8697–8710.
9. Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. IEEE CVPR, 6848–6856.
10. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
11. Graves, A., & Schmiduber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5), 602–610.
12. Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–72.
13. Bhattacharya, S., Ghosh, S., & Chakraborty, S. (2020). Brain hemorrhage detection from CT images using deep neural networks. International Journal of Imaging Systems and Technology, 30(3), 635– 645.
14. Liang, X., Chen, T., Chen, Y., & Li, Y. (2021). Automatic ischemic stroke detection using CNN features and optimized classifiers. Biomedical Signal Processing and Control, 68, 102–118.
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