Abstract
Today's technologies have deeply influenced human health and daily life. Consequently, the health care process is widely improved to automate and detect diseases. Deep Learning and Transfer Learning classifiers are within emergent technologies that impact health care. In this paper, we used Transfer Learning and Convolutional Neural Network to classify and detect the Intracranial hemorrhage (ICH). The performance of the used classifiers is evaluated and compared on the Intracranial Hemorrhage Dataset that contains 2814 images. The results show that the detection accuracy of Transfer Learning with Inception V3, which achieves 88.97%, is superior to that of the Convolutional neural network.
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
Similar content being viewed by others
References
Ye, H., et al.: Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. Eur. Radiol. 29(11), 6191–6201 (2019). https://doi.org/10.1007/s00330-019-06163-2
Kuo, W., Hӓne, C., Mukherjee, P., Malik, J., Yuh, E.L.: Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning. Proc. Natl. Acad. Sci. 116(45), 22737–22745 (2019)
Tekouabou, S.C.K., Hartini, S., Rustam, Z., Silkan, H., Agoujil, S.: Improvement in automated diagnosis of soft tissues tumors using machine learning. Big Data Min. Anal. 4(1), 33–46 (2021)
Dawud, A.M., Yurtkan, K., Oztoprak, H.: Application of deep learning in neuroradiology: brain haemorrhage classification using transfer learning. Comput. Intell. Neurosci. 2019 (2019)
Burduja, M., Ionescu, R.T., Verga, N.: 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 (2020)
Toğaçar, M., Cömert, Z., Ergen, B., Budak, Ü.: Brain hemorrhage detection based on heat maps, autoencoder and CNN architecture. In: 2019 1st International Informatics and Software Engineering Conference (UBMYK), pp. 1–5. IEEE, November 2009
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, London (2016)
Ker, J., Singh, S.P., Bai, Y., Rao, J., Lim, T., Wang, L.: Image thresholding improves 3-dimensional convolutional neural network diagnosis of different acute brain hemorrhages on computed tomography scans. Sensors 19(9), 2167 (2019)
Castro, J.S., Chabert, S., Saavedra, C., Salas, R.F.: Convolutional neural networks for detection intracranial hemorrhage in CT images. In: CRoNe, pp. 37–43 (2019)
Lewick, T., Kumar, M., Hong, R., Wu, W.: Intracranial hemorrhage detection in CT scans using deep learning. In: 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 169–172. IEEE, August 2020
Nguyen, N.T., Tran, D.Q., Nguyen, N.T., Nguyen, H.Q.: A CNN-LSTM architecture for detection of intracranial hemorrhage on CT scans. arXiv preprint arXiv:2005.10992 (2020)
Chilamkurthy, S., et al.: Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 392(10162), 2388–2396 (2018)
Tékouabou, S.C.K., Chabbar, I., Toulni, H., Cherif, W., Silkan, H.: Optimizing the early glaucoma detection from visual fields by combining preprocessing techniques and ensemble classifier with selection strategies. Expert Syst. Appl. 189, 115975 (2022)
Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In International conference on machine learning. In: PMLR, pp. 97–105. 2015, June
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2012)
Cheng, P.M., Malhi, H.S.: Transfer learning with convolutional neural networks for classification of abdominal ultrasound images. J. Digit. Imaging 30(2), 234 (2017)
Lei, H., et al.: A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer learning. Pattern Recogn. 79, 290–302 (2018)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Kumar, A., Kim, J., Lyndon, D., Fulham, M., Feng, D.: An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J. Biomed. Health Inform. 21(1), 31–40 (2016)
Nguyen, L.D., Lin, D., Lin, Z., Cao, J.: Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5. IEEE, May 2018
Hssayeni, M.D., Croock, M.S., Salman, A.D., Al-khafaji, H.F., Yahya, Z.A., Ghoraani, B.: Intracranial hemorrhage segmentation using a deep convolutional model. Data 5(1), 14 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
El Mouna, L., Silkan, H., Haynf, Y., Tmiri, A., Dahmouni, A. (2022). Comparative Study of Deep Learning Models for Detection and Classification of Intracranial Hemorrhage. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2022. Lecture Notes in Business Information Processing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-06458-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-06458-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06457-9
Online ISBN: 978-3-031-06458-6
eBook Packages: Computer ScienceComputer Science (R0)