Abstract
Intracranial hemorrhage (ICH) is an acute-stroke type leading to a high mortality rate. ICH diagnoses can keep patients out of life-threatening and longlasting consequences. These diagnoses are typically detected by analyzing images of Computed Tomography (CT) scan and symptoms of the patients. Consequently, the expertise of radiologists is significant in terms of detecting signals of ICH and making in-time decisions to save these patients. Over the past few decades, due to powerful-and-modern machine learning algorithms, several computer-aided detection (CAD) systems have been developed. They are becoming an effective part of routine clinical work to detect a few critical diseases, such as strokes, cancers, and so on. In this paper, we introduce a CAD that combines a deep-learning model and typical image processing techniques to determine whether patients suffer from ICH due to their CT images. The deep-learning model based on MobileNetV2 architecture was trained on RSNA (Radiological Society of North America) Intracranial Hemorrhage dataset. Then it was validated on a dataset of ICH-Vietnamese cases collected from Vinh Long Province Hospital, Vietnam. The experiment indicated that the classifier achieved AUC of 0.991, sensitivity of 0.992, and specificity of 0.807. After the deep-learning model identifies ICH-suspected slices, the Hounsfield Unit (HU) method is employed to highlight specific hemorrhage areas in these slices. DBSCAN is also used to remove noises detected by the HU method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Phong, T.D., Duong, H.N., Nguyen, H.T., Trong, N.T., Nguyen, V.H., Van Hoa, T., Snasel, V.: Brain hemorrhage diagnosis by using deep learning. In: Proceedings of the 2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017, pp. 34–39. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3036290.3036326
Munn, Z., Moola, S., Lisy, K., Riitano, D., Murphy, F.: Claustrophobia in magnetic resonance imaging: a systematic review and meta-analysis. Radiography 21(2), e59–e63 (2015)
Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9(1), 1–12 (2019)
Wu, N., Phang, J., Park, J., Shen, Y., Huang, Z., Zorin, M., Jastrzębski, S., Févry, T., Katsnelson, J., Kim, E., et al.: Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging 39(4), 1184–1194 (2019)
Polson, N., Sokolov, V.: Deep learning for short-term traffic flow prediction. Transp. Res. Part C: Emerg. Technol. 79, 1–17 (2017)
Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2018)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Akter, M., Hirai, T., Hiai, Y., Kitajima, M., Komi, M., Murakami, R., Fukuoka, H., Sasao, A., Toya, R., Haacke, E.M., et al.: Detection of hemorrhagic hypointense foci in the brain on susceptibility-weighted imaging: clinical and phantom studies. Acad. Radiol. 14(9), 1011–1019 (2007)
Imielinska, C., Liu, X., Rosiene, J., Sughrue, M., Komotar, R., Mocco, J., Ransom, E., Lignelli, A., Zacharia, B., Connolly, E., D’Ambrosio, A.: Toward objective quantification of perfusion-weighted computed tomography in subarachnoid hemorrhage: quantification of symmetry and automated delineation of vascular territories1. Acad. Radiol. 12, 874–87 (2005)
Phan, T.G., Donnan, G.A., Koga, M., Mitchell, L.A., Molan, M., Fitt, G., Chong, W., Holt, M., Reutens, D.C.: The aspects template is weighted in favor of the striatocapsular region. Neuroimage 31(2), 477–481 (2006)
Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput. Med. Imaging Graph. 31(4–5), 285–298 (2007)
Gong, T., Liu, R., Tan, C.L., Farzad, N., Lee, C.K., Pang, B.C., Tian, Q., Tang, S., Zhang, Z.: Classification of CT brain images of head trauma. In: IAPR International Workshop on Pattern Recognition in Bioinformatics, pp. 401–408. Springer, Cham (2007)
Trivizakis, E., Manikis, G., Nikiforaki, K., Drevelegas, K., Constantinides, M., Drevelegas, A., Marias, K.: Extending 2-D convolutional neural networks to 3-D for advancing deep learning cancer classification with application to MRI liver tumor differentiation. IEEE J. Biomed. Health Inf. 23(3), 923–930 (2018)
Lee, H., Yune, S., Mansouri, M., Kim, M., Tajmir, S.H., Guerrier, C.E., Ebert, S.A., Pomerantz, S.R., Romero, J.M., Kamalian, S., et al.: An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat. Biomed. Eng. 3(3), 173 (2019)
Chawla, M., Sharma, S., Sivaswamy, J., Kishore, L.: A method for automatic detection and classification of stroke from brain CT images. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3581–3584. IEEE (2009)
Jnawali, K., Arbabshirani, M.R., Rao, N., Patel, A.A.: Deep 3D convolution neural network for CT brain hemorrhage classification. In: Petrick, N., Mori, K. (eds.) Medical Imaging 2018: Computer-Aided Diagnosis, vol. 10575, pp. 307 – 313. International Society for Optics and Photonics, SPIE (2018). https://doi.org/10.1117/12.2293725
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning, ICML 2015, vol. 37, pp. 448–456. JMLR.org (2015)
Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143–155 (2002)
Jenkinson, M., Pechaud, M., Smith, S., et al.: BET2: MR-based estimation of brain, skull and scalp surfaces. In: Eleventh Annual Meeting of the Organization for Human Brain Mapping, Toronto, vol. 17, p. 167 (2005)
Acknowledgement
The authors would like to thank doctors working at the Para-Clinical Department, Vinh Long Hospital, Vietnam who provided expertise and imagery data as well as greatly assisted the study. This study was funded by Vinh Long Department of Science and Technology, under contract number 03/HÐ-2019.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Luong, K.G. et al. (2021). A Computer-Aided Detection to Intracranial Hemorrhage by Using Deep Learning: A Case Study. In: Huang, YP., Wang, WJ., Quoc, H.A., Giang, L.H., Hung, NL. (eds) Computational Intelligence Methods for Green Technology and Sustainable Development. GTSD 2020. Advances in Intelligent Systems and Computing, vol 1284. Springer, Cham. https://doi.org/10.1007/978-3-030-62324-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-62324-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62323-4
Online ISBN: 978-3-030-62324-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)