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on heterogeneous systems, 2015. Software available D. Cournapeau, M. Brucher, M. Perrot, and
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Python. Journal of Machine Learning Research,
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hemorrhage, Accessed: Benign and malignant breast tumors classification
2019-02-21. based on region growing and cnn segmentation. Expert
Systems with Applications, 42(3):990–1002, 2015.
[9] I. Fister Jr, X.-S. Yang, I. Fister, J. Brest, and
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for optimization. arXiv preprint arXiv:1307.4186, Pressure Management for Acute Intracerebral
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Computer-Aided Diagnosis, volume 10575, page building nature-inspired algorithms. Journal of Open
105751C. International Society for Optics and Source Software, 3, 2018.
Photonics, 2018.
[30] G. Vrbancic, I. J. Fister, and V. Podgorelec.
[15] A. Krizhevsky. One weird trick for parallelizing Automatic Detection of Heartbeats in Heart Sound
convolutional neural networks. arXiv preprint Signals Using Deep Convolutional Neural Networks.
arXiv:1404.5997, 2014. Elektronika ir Elektrotechnika, 25(3):71–76, jun 2019.
[16] D. Larsen-Freeman. Transfer of learning transformed. [31] G. Vrbancic and V. Podgorelec. Automatic
Language Learning, 63:107–129, 2013. Classification of Motor Impairment Neural Disorders
from EEG Signals Using Deep Convolutional Neural
[17] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al. Networks. Elektronika ir Elektrotechnika, 24(4):3–7,
Gradient-based learning applied to document aug 2018.
recognition. Proceedings of the IEEE,
86(11):2278–2324, 1998. [32] G. Vrbanˇciˇc, I. Fister, Jr., and V. Podgorelec. Swarm
intelligence approaches for parameter setting of deep
[18] W. McKinney. Data structures for statistical learning neural network: Case study on phishing
computing in python. In S. van der Walt and websites classification. In Proceedings of the 8th
J. Millman, editors, Proceedings of the 9th Python in International Conference on Web Intelligence, Mining
Science Conference, pages 51 – 56, 2010. and Semantics, WIMS ’18, pages 9:1–9:8, New York,
NY, USA, 2018. ACM.
[19] S. Mirjalili, S. M. Mirjalili, and A. Lewis. Grey wolf
optimizer. Advances in engineering software, 69:46–61,
2014.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 66
Koper, Slovenia, 10 October
convolutional neural network for hemorrhage Wolf-pack (canis lupus) hunting strategies emerge
evaluation on head ct. American Journal of from simple rules in computational simulations.
Neuroradiology, 39(9):1609–1616, 2018. Behavioural processes, 88(3):192–197, 2011.
[5] F. Chollet et al. Keras, 2015. [21] K. Nogueira, O. A. Penatti, and J. A. dos Santos.
Towards better exploiting convolutional neural
[6] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and networks for remote sensing scene classification.
L. Fei-Fei. Imagenet: A large-scale hierarchical image Pattern Recognition, 61:539–556, 2017.
database. In 2009 IEEE conference on computer vision
and pattern recognition, pages 248–255. Ieee, 2009. [22] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel,
B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer,
[7] M. A. et al. TensorFlow: Large-scale machine learning R. Weiss, V. Dubourg, J. Vanderplas, A. Passos,
on heterogeneous systems, 2015. Software available D. Cournapeau, M. Brucher, M. Perrot, and
from tensorflow.org. E. Duchesnay. Scikit-learn: Machine learning in
Python. Journal of Machine Learning Research,
[8] Felipe Kitamura. Head CT - hemorrhage, 2018. 12:2825–2830, 2011.
Available at
https://www.kaggle.com/felipekitamura/head-ct- [23] R. Rouhi, M. Jafari, S. Kasaei, and P. Keshavarzian.
hemorrhage, Accessed: Benign and malignant breast tumors classification
2019-02-21. based on region growing and cnn segmentation. Expert
Systems with Applications, 42(3):990–1002, 2015.
[9] I. Fister Jr, X.-S. Yang, I. Fister, J. Brest, and
D. Fister. A brief review of nature-inspired algorithms [24] L. Shi, S. Xu, J. Zheng, J. Xu, and J. Zhang. Blood
for optimization. arXiv preprint arXiv:1307.4186, Pressure Management for Acute Intracerebral
2013. Hemorrhage: A Meta-Analysis. Scientific Reports,
7(1):14345, 2017.
[10] K. Fukushima. Neocognitron: A self-organizing neural
network model for a mechanism of pattern recognition [25] H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu,
unaffected by shift in position, BioL Cybem. 36 (1980) I. Nogues, J. Yao, D. Mollura, and R. M. Summers.
193-202. S. Shiotani et al./Neurocomputing 9 (1995) Deep convolutional neural networks for
Ill-130, 130, 1980. computer-aided detection: Cnn architectures, dataset
characteristics and transfer learning. IEEE
[11] M. Grewal, M. M. Srivastava, P. Kumar, and transactions on medical imaging, 35(5):1285–1298,
S. Varadarajan. Radnet: Radiologist level accuracy 2016.
using deep learning for hemorrhage detection in ct
scans. In 2018 IEEE 15th International Symposium on [26] K. Simonyan and A. Zisserman. Very deep
Biomedical Imaging (ISBI 2018), pages 281–284. convolutional networks for large-scale image
IEEE, 2018. recognition. arXiv preprint arXiv:1409.1556, 2014.
[12] A. Helwan, G. El-Fakhri, H. Sasani, and [27] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed,
D. Uzun Ozsahin. Deep networks in identifying ct D. Anguelov, D. Erhan, V. Vanhoucke, and
brain hemorrhage. Journal of Intelligent & Fuzzy A. Rabinovich. Going deeper with convolutions. In
Systems, (Preprint):1–1, 2018. Proceedings of the IEEE conference on computer
vision and pattern recognition, pages 1–9, 2015.
[13] M. Hussain, J. J. Bird, and D. R. Faria. A study on
cnn transfer learning for image classification. In UK [28] S. Van Der Walt, S. C. Colbert, and G. Varoquaux.
Workshop on Computational Intelligence, pages The numpy array: a structure for efficient numerical
191–202. Springer, 2018. computation. Computing in Science & Engineering,
13(2):22, 2011.
[14] K. Jnawali, M. R. Arbabshirani, N. Rao, and A. A.
Patel. Deep 3d convolution neural network for ct brain [29] G. Vrbanˇciˇc, L. Brezoˇcnik, U. Mlakar, D. Fister, and
hemorrhage classification. In Medical Imaging 2018: I. Fister Jr. NiaPy: Python microframework for
Computer-Aided Diagnosis, volume 10575, page building nature-inspired algorithms. Journal of Open
105751C. International Society for Optics and Source Software, 3, 2018.
Photonics, 2018.
[30] G. Vrbancic, I. J. Fister, and V. Podgorelec.
[15] A. Krizhevsky. One weird trick for parallelizing Automatic Detection of Heartbeats in Heart Sound
convolutional neural networks. arXiv preprint Signals Using Deep Convolutional Neural Networks.
arXiv:1404.5997, 2014. Elektronika ir Elektrotechnika, 25(3):71–76, jun 2019.
[16] D. Larsen-Freeman. Transfer of learning transformed. [31] G. Vrbancic and V. Podgorelec. Automatic
Language Learning, 63:107–129, 2013. Classification of Motor Impairment Neural Disorders
from EEG Signals Using Deep Convolutional Neural
[17] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al. Networks. Elektronika ir Elektrotechnika, 24(4):3–7,
Gradient-based learning applied to document aug 2018.
recognition. Proceedings of the IEEE,
86(11):2278–2324, 1998. [32] G. Vrbanˇciˇc, I. Fister, Jr., and V. Podgorelec. Swarm
intelligence approaches for parameter setting of deep
[18] W. McKinney. Data structures for statistical learning neural network: Case study on phishing
computing in python. In S. van der Walt and websites classification. In Proceedings of the 8th
J. Millman, editors, Proceedings of the 9th Python in International Conference on Web Intelligence, Mining
Science Conference, pages 51 – 56, 2010. and Semantics, WIMS ’18, pages 9:1–9:8, New York,
NY, USA, 2018. ACM.
[19] S. Mirjalili, S. M. Mirjalili, and A. Lewis. Grey wolf
optimizer. Advances in engineering software, 69:46–61,
2014.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 66
Koper, Slovenia, 10 October
