Hybrid Convolutional Neural Network-Based Diagnosis System for Intracranial Hemorrhage
DOI:
https://doi.org/10.18662/brain/12.4/236Keywords:
Deep Convolutional Neural Network, Intracranial Hemorrhage, Computer Tomography, Inception-ResNet-V2, EfficientNet-B3Abstract
Early diagnosis of intracranial hemorrhage significantly reduces mortality. Hemorrhage is diagnosed by using various imaging methods and the most time-efficient one among them is computed tomography (CT). However, it is clear that accurate CT scans requires time, diligence, and experience. Computer-aided design methods are vital for the treatment because they facilitate early diagnosis of intracranial hemorrhage. At this point, deep learning can provide effective outcomes through an automated diagnosis way. However, as different from the known solutions, diagnosis of five different hemorrhage subtypes is a critical problem to be solved.This study focused on deep learning methods and employed cranial computed tomography scans in order to detect intracranial hemorrhage. The diagnosis approach in the study aimed to detect five subtypes of hemorrhage. In detail, EfficientNet-B3 and ResNet-Inception-V2 architectures were used for diagnosis purposes. Eventually, the study also proposed a two-architecture hybrid method for the diagnosis purpose. The obtained findings by the hybrid method were evaluated in terms of a comparative perspective.Results showed that the newly designed hybrid method was quite effective in terms of increasing classification rates of detecting intracranial hemorrhage according to the subtypes. Briefly, an accuracy of 98.5%, which is higher than those of the EfficientNet-B3 and the Inception-ResNet-V2, were obtained thanks to the developed hybrid method.
References
Al-Ayyoub, M., Alawad, D., Al-Darabsah, K., & Aljarrah, I. (2013). Automatic detection and classification of brain hemorrhages. WSEAS transactions on computers, 12(10), 395-405. http://www.wseas.us/journal/pdf/computers/2013/b065705-365.pdf
Anevrizma Yırtılması Ve Beyin Kanamasına Dikkat. (n.d). Medicine Hospital. Retrieved March 31, 2020, from https://www.medicinehospital.com.tr/blog/anevrizma-yirtilmasi-ve-beyin-kanamasina-dikkat
Arbabshirani, M. R., Fornwalt, B. K., Mongelluzzo, G. J., Suever, J. D., Geise, B. D., Patel, A. A., & Moore, G. J. (2018). Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. Digital Medicine, 1, 9. https://doi.org/10.1038/s41746-017-0015-z
Buduruja, M., Ionescu, R. T., Verga, N. (2020). Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks. Sensors, 20(19), 5611. http://dx.doi.org/10.3390/s20195611
Chang, P., Kuoy, E., Grinband, J., Weinberg, B., Thompson, M., Homo, R., Chen, J., Abcede, H., Shafie, M., Sugrue, L., Filippi, C. G., Su, M.-Y., Hess, C., & Chow, D. (2018). Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. American Journal of Neuroradiology, 39(9), 1609-1616. https://doi.org/10.3174/ajnr.A5742
Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K., Mahajan, V., Rao, P., & Warier, P. (2018). Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. The Lancet, 392(10162), 2388-2396. https://www.mlgdansk.pl/wp-content/uploads/2019/06/MLGdansk63_27.05.19_PIIS0140673618316453.pdf
Gardner, M. A., Li, B. C., Wu, Y. W., & Slavotinek, A. M. (2012). Intraparenchymal hemorrhage in a neonate with cleidocranial dysostosis. Pediatric Neurology, 47(6), 455-457. https://doi.org/10.1016/j.pediatrneurol.2012.08.009
Grewal, M., Srivastava, M. M., Kumar, P., & Varadarajan, S. (2018, April). Radnet: Radiologist level accuracy using deep learning for hemorrhage detection in ct scans. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 281-284). IEEE. https://arxiv.org/pdf/1710.04934.pdf
Helwan, A., El-Fakhri, G., Sasani, H., & Uzun Ozsahin, D. (2018). Deep networks in identifying CT brain hemorrhage. Journal of Intelligent & Fuzzy Systems, 35(2), 2215-2228. http://doi.org/10.3233/jifs-172261
ImageNet. (n.d.). Retrieved March 31, 2020, from http://www.image-net.org/
Jnawali, K., Arbabshirani, M. R., Rao, N., & Patel, A. A. (2018, February). Deep 3D convolution neural network for CT brain hemorrhage classification. In Medical Imaging 2018: Computer-Aided Diagnosis, 10575, (p. 105751C). International Society for Optics and Photonics. https://doi.org/10.1117/12.2293725
Jauch, E. C., Saver, J. L., Adams, H. P., Bruno, A., Connors, J. J. B., Demaerschalk, B. M., Khatri, P., McMullan, P. W., Qureshi, A. I., Rosenfield, K., Scott, P. A., Summers, D. R., Wang, D. Z., Wintermark, M., & Yonas, H. (2013). Guidelines for the early management of patients with acute ischemic stroke: A guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 44(3), 870-947. https://doi.org/10.1161/STR.0b013e318284056a
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., Gonzalez, R. G., Lev, M. H., & Do, S. (2019). An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nature Biomedical Engineering, 3, 173-182. https://doi.org/10.1038/s41551-018-0324-9
Majumdar, A., Brattain, L., Telfer, B., Farris, C., & Scalera, J. (2018). Detecting Intracranial Hemorrhage with Deep Learning. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 583-587. http://doi.org/10.1109/embc.2018.8512336
Muir, K. W., Buchan, A., von Kummer, R., Rother, J., & Baron, J. C. (2006). Imaging of acute stroke. Lancet Neurology, 5(9), 755-768. https://doi.org/10.1016/S1474-4422(06)70545-2
Phong, T. D., Trong, N. T., Duong, H. N., Nguyen, V. H., Snasel, V., Nguyen, H. T., & Van Hoa, T. (2017). Brain hemorrhage diagnosis by using deep learning. ACM International Conference Proceeding Series, 34-39. https://doi.org/10.1145/3036290.3036326
Prevedello, L. M., Erdal, B. S., Ryu, J. L., Little, K. J., Demirer, M., Qian, S., & White, R. D. (2017). Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology, 285(3), 923-931. https://pubs.rsna.org/doi/pdf/10.1148/radiol.2017162664
RSNA Intracranial Hemorrhage Detection. (n.d.). Retrieved March 31, 2020, from https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection
Salehinejad, H., Kitamura, J., Ditkofsky, N., Lin, A., Bharatha, A., Suthiphosuwan, S., Lin, H.-M., Wilson, J. R., Mamdani, M., & Colak, E. (2021). A Real-World Demonstration of Machine Learning Generalizability: Intracranial Hemorrhage Detection on Head CT, 1-15. https://arxiv.org/ftp/arxiv/papers/2102/2102.04869.pdf
Şeker, A., Diri, B., & Balik, H. (2017). Derin Öğrenme Yöntemleri Ve Uygulamaları Hakkında Bir İnceleme [A Review of Deep Learning Methods and Applications]. Gazi Mühendislik Bilimleri Dergisi (GMBD), 3(3), 47-64. https://dergipark.org.tr/tr/download/article-file/394923
Shahangian, B., & Pourghassem, H. (2016). Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure. Biocybernetics and Biomedical Engineering, 36(1), 217-232. http://doi.org/10.1016/j.bbe.2015.12.001
Stanford.edu. (2018). Convolutional Neural Networks for Visual Recognition [class course]. http://cs231n.stanford.edu/
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 4278-4284. https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14806/14311
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. http://proceedings.mlr.press/v97/tan19a/tan19a.pdf
Web of Science [v.5.34] - Web of Science Core Collection Results. (n.d.). Retrieved March 31, 2020, from http://proxy.afyon.deep-knowledge.net/MuseSessionID=0210h3diw/MuseProtocol=http/MuseHost=apps.webofknowledge.com
Ye, H., Gao, F., Yin, Y., Guo, D., Zhao, P., Lu, Y., Wang, X., Bai, J., Cao, K., Song, Q., Zhang, H., Chen, W., Guo, X., & Xia, J. (2019). Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. European Radiology, 29(11), 6191-6201. https://doi.org/10.1007/s00330-019-06163-2
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