Skip to main content

Advertisement

SpringerLink
Log in

A Dense-Layered Deep Neural Model-Based Classification of Brain Hemorrhages Using Head Computer Tomography Images

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Artificial Intelligence (AI) refers to the ability to learn, remember, predict, and make an optimal judgment based on Computer-assisted Design (CAD) Systems. Traditional CAD algorithms and methods on head CT scans focused on the automatic recognition, segmentation, and classification of the abnormalities. But, these approaches were encountered with several limitations like (i) smaller dataset size, (ii) negative Transfer Learning, and (iii) improper localization. This paper proposed the new dense-layered deep neural model to classify brain hemorrhages using head CT scans. The proposed model is the ten-layered network having dense blocks with skip connections. It uses the cross-chained connection between dense blocks to minimize gradient loss while training. Later, the last layer of the model is extended with Grad-Cam for localization of the affected cell regions. The model performance is evaluated on a dataset of head CT scans of size 427.25GB. The dataset is partitioned into 752,800 images in the training set and 121,232 images in the testing set. The experimentation results achieved an accuracy of 98.32% with a mean logarithmic loss of 0.06487. The average classification accuracy of the proposed model on multiple-class hemorrhages is 98.27%. The experimentation results are found satisfactory having the best AUC-ROC accuracy of 98.32%. The comparative analysis of the model with other traditional deep neural networks proves the efficacy of the model in predicting results. Also, in comparison with other methods, the gained results are found satisfactory with an increase in the accuracy of 1.3%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

No data is associated with this work as the dataset is taken from the Kaggle platform. However, the implementation code can be made available on reasonable request to the corresponding author.

Notes

  1. https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/data

References

  1. Caceres JA, Goldstein JN. Intracranial hemorrhage. Emerg Med Clin North Am. 2012;30(3):771.

    Article  Google Scholar 

  2. Mahmud M, Kaiser MS, Hussain A, Vassanelli S. Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst. 2018;29(6):2063–79.

    Article  MathSciNet  Google Scholar 

  3. Mahmud M, Kaiser MS, McGinnity TM, Hussain A. Deep learning in mining biological data. Cogn Comput. 2021;13(1):1–33.

    Article  Google Scholar 

  4. Ali HM, Kaiser MS, Mahmud M. Application of convolutional neural network in segmenting brain regions from MRI data. In: International conference on brain informatics. Springer; 2019. p. 136–46.

  5. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.

    Article  Google Scholar 

  6. Chan T. Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput Med Imaging Graph. 2007;31(4–5):285–98.

    Article  Google Scholar 

  7. Yuh EL, Gean AD, Manley GT, Callen AL, Wintermark M. Computer-aided assessment of head computed tomography (CT) studies in patients with suspected traumatic brain injury. J Neurotrauma. 2008;25(10):1163–72.

    Article  Google Scholar 

  8. Lee H, Yune S, Mansouri M, Kim M, Tajmir SH, Guerrier CE, Ebert SA, Pomerantz SR, Romero JM, Kamalian S, et al. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng. 2019;3(3):173.

    Article  Google Scholar 

  9. Gautam A, Raman B. Automatic segmentation of intracerebral hemorrhage from brain CT images. In: Machine Intelligence and Signal Analysis. Springer; 2019. p. 753–64.

  10. Hussain S, Anwar SM, Majid M. Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing. 2018;282:248–61.

    Article  Google Scholar 

  11. Abiyev RH, Arslan M. Head mouse control system for people with disabilities. Expert Syst. 2020;37(1):e12398.

  12. Helwan A, El-Fakhri G, Sasani H, Uzun Ozsahin D. Deep networks in identifying CT brain hemorrhage. J Intell Fuzzy Syst. 2018;35(2):2215–28.

    Article  Google Scholar 

  13. Gong T, Liu R, Tan CL, Farzad N, Lee CK, Pang BC, Tian Q, Tang S, Zhang Z. Classification of CT brain images of head trauma. In: IAPR International Workshop on Pattern Recognition in Bioinformatics. Springer; 2007. p. 401–8.

  14. Jnawali K, Arbabshirani MR, Rao N, Patel AA. Deep 3D convolution neural network for CT brain hemorrhage classification. In: Medical Imaging 2018: Computer-Aided Diagnosis, vol. 10575. International Society for Optics and Photonics; 2018. p. 105751C.

  15. Cho J, Park KS, Karki M, Lee E, Ko S, Kim JK, Lee D, Choe J, Son J, Kim M, et al. Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models. J Digit Imaging. 2019;32(3):450–61.

    Article  Google Scholar 

  16. Kuo W, Häne C, Yuh E, Mukherjee P, Malik J. Cost-sensitive active learning for intracranial hemorrhage detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2018. p. 715–23.

  17. Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, White RD. Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology. 2017;285(3):923–31.

    Article  Google Scholar 

  18. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. 2012; p. 1097–105.

  19. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 [Preprint]. 2014. Available from: http://arxiv.org/abs/1409.1556.

  20. Ruiz J, Mahmud M, Modasshir M, Shamim Kaiser M, Alzheimer’s disease Neuroimaging Initiative, FT et al. 3D densenet ensemble in 4-way classification of Alzheimer’s disease. In: International Conference on Brain Informatics. Springer; 2020. p. 85–96.

  21. 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. 2016. p. 770–8.

  22. Ye H, Gao F, Yin Y, Guo D, Zhao P, Lu Y, Wang X, Bai J, Cao K, Song Q, et al. Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. Eur Radiol. 2019;29(11):6191–201.

    Article  Google Scholar 

  23. Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, Moore GJ. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med. 2018;1(1):1–7.

    Article  Google Scholar 

  24. Grewal M, Srivastava MM, Kumar P, Varadarajan S. Radnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT Scans. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE; 2018. p 281–4.

  25. DenOtter TD, Schubert J (2019) Hounsfield unit. In: StatPearls [Internet]. StatPearls Publishing

  26. Razi T, Niknami M, Ghazani FA. Relationship between hounsfield unit in CT scan and gray scale in CBCT. J Dent Res Dent Clin Dent Prospects. 2014;8(2):107.

    Google Scholar 

  27. Huang G, Liu Z, Van DerMaaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. p. 4700–8.

  28. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision. 2017. p. 618–26.

  29. Desai V, Flanders AE, Lakhani P. Application of deep learning in neuroradiology: automated detection of basal ganglia hemorrhage using 2D-convolutional neural networks. arXiv:1710.03823 [Preprint]. 2017. Available from: https://arxiv.org/abs/1710.03823.

  30. Tong HL, Fauzi MFA, Haw SC, Ng H, Yap TTV. Automatic classification and retrieval of brain hemorrhages. In: Computational Science and Technology. Springer; 2019. p. 1–11.

  31. Chang PD, Kuoy E, Grinband J, Weinberg BD, Thompson M, Homo R, Chen J, Abcede H, Shafie M, Sugrue L, et al. Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. Am J Neuroradiol. 2018;39(9):1609–16.

    Article  Google Scholar 

  32. Lee JY, Kim JS, Kim TY, Kim YS. Detection and classification of intracranial haemorrhage on ct images using a novel deep-learning algorithm. Sci Rep. 2020;10(1):1–7.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE &IT, Jaypee Instutute of Information Technology, Noida, India

    Ankit Vidyarthi

Authors
  1. Ankit Vidyarthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ankit Vidyarthi.

Ethics declarations

Ethics Statement

The author of this manuscript confirms the following: (i) Informed, written consent has been obtained from the relevant sources wherever is required; (ii) All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964 and its later amendments. (iii) The approval and/or informed consent was not required for study as the dataset is collected from the open source Kaggle website, a well known data repository for worldwide data analytics problem and live challenges.

Conflict of Interest

The author declares no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vidyarthi, A. A Dense-Layered Deep Neural Model-Based Classification of Brain Hemorrhages Using Head Computer Tomography Images. Cogn Comput 15, 1042–1052 (2023). https://doi.org/10.1007/s12559-022-10090-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-022-10090-8

Keywords

Search

Navigation