Skip to main content
SpringerLink
Log in

An optimal segmentation with deep learning based inception network model for intracranial hemorrhage diagnosis

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Traumatic Brain Injury (TBI) leads to intracranial hemorrhages (ICH), which is a severe illness resulted in death if it is not properly diagnosed and treated in the earlier stage. Presently, computer tomography (CT) images are widely used by radiologists to identify and locate the regions of ICH. But it is a tedious task and mainly depends on the professional radiologists. This paper develops new deep learning (DL)-based ICH diagnosis and classification (DL-ICH) model using optimal image segmentation with Inception Network. The proposed DL-ICH model involves preprocessing, segmentation, feature extraction, and classification. Firstly, the input data undergo format conversion where the NIfTI files are converted into JPEG format. Next, Kapur's thresholding with an elephant herd optimization (EHO) algorithm called KT-EHO is employed for image segmentation. Then, DL based Inception v4 network is applied as a feature extractor to extract a useful set of features and a multilayer perceptron (MLP) is used for classification. An extensive set of simulations takes place to ensure the effective diagnostic performance of the DL-ICH model and the results are investigated under diverse dimensions. The experimental results achieved better accuracy rate.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Taylor CA, Bell JM, Breiding MJ, Xu L (2017) Traumatic brain injury-related emergency department visits, hospitalizations, and deaths-United States, 2007 and 2013. Morb Mortal Wkly Rep SurveillSumm 66:1–16

    Article  Google Scholar 

  2. Yuh EL, Gean AD, Manley GT, Callen AL, Wintermark M (2008) Computer-aided assessment of head computed tomography (CT) studies in patients with suspected traumatic brain injury. J Neurotrauma 25:1163–1172

    Article  Google Scholar 

  3. Li Y, Wu J, Li H, Li D, Du X, Chen Z, Jia F, Hu Q (2012) Automatic detection of the existence of subarachnoid hemorrhage from clinical CT images. J Med Syst 36:1259–1270

    Article  Google Scholar 

  4. Kuo W, Häne C, Yuh E, Mukherjee P, Malik J (2018) Cost-sensitive active learning for intracranial hemorrhage detection. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G (eds) Medical image computing and computer assisted intervention—MICCAI 2018. Springer, Cham, pp 715–723

    Chapter  Google Scholar 

  5. Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, White RD (2017) Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 285:923–931

    Article  Google Scholar 

  6. Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, Mahajan V, Rao P, Warier P (2018) Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 392:2388–2396

    Article  Google Scholar 

  7. Grewal M, Srivastava MM, Kumar P, Varadarajan S (2018) RADnet: radiologist level accuracy using deep learning for hemorrhage detection in CT scans. In: Proceedings of the 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), Washington, DC, USA, 4–7 Apr 2018, pp 281–284

  8. Ye H, Gao F, Yin Y, Guo D, Zhao P, Lu Y, Wang X, Bai J, Cao K, Song Q et al (2019) Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. EurRadiol 29:6191–6201

    Google Scholar 

  9. Jnawali K, Arbabshirani MR, Rao N, Patel AA (2018) Deep 3D convolution neural network for CT brain hemorrhage classification. In: Medical imaging 2018: computer-aided diagnosis; international society for optics and photonics: Washington, DC, USA, vol 10575, p 105751C

  10. Chang P, Kuoy E, Grinband J, Weinberg B, Thompson M, Homo R, Chen J, Abcede H, Shafie M, Sugrue L et al (2018) Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. Am J Neuroradiol 39:1609–1616

    Article  Google Scholar 

  11. Rajinikanth V, Satapathy SC, Fernandes SL, Nachiappan S (2017) Entropy based segmentation of tumor from brain MR images—a study with teaching learning based optimization. Pattern RecognLett 94:87–95

    Article  Google Scholar 

  12. Karolin M, Meyyappan T (2019) Secret multiple share creation with color images using visual cryptography. In: 2019 International conference on communication and signal processing (ICCSP). IEEE, pp 0058–0062

  13. https://towardsdatascience.com/review-inception-v4-evolved-from-googlenet-merged-with-resnet-idea-image-classification-5e8c339d18bc

  14. Tekchandani H, Verma S, Londhe N (2020) Performance improvement of mediastinal lymph node severity detection using GAN and Inception network. Comput Methods Programs Biomed 194:105478

    Article  Google Scholar 

  15. Lydia A, Francis S (2019) Adagrad—an optimizer for stochastic gradient descent. Int J Inf Comput Sci 6(5)

  16. Heidari AA, Faris H, Aljarah I, Mirjalili S (2019) An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Comput 23(17):7941–7958

    Article  Google Scholar 

  17. Hssayeni MD, Croock MS, Salman AD, Al-khafaji HF, Yahya ZA, Ghoraani B (2020) Intracranial hemorrhage segmentation using a deep convolutional model. Data 5(1):14

    Article  Google Scholar 

  18. Davis V, Devane S (2017) Diagnosis & classification of brain hemorrhage. In: 2017 International conference on advances in computing, communication and control (ICAC3). IEEE, pp 1–6

  19. Danilov G, Kotik K, Negreeva A, Tsukanova T, Shifrin M, Zakharova N, Batalov A, Pronin I, Potapov A (2020) Classification of intracranial hemorrhage subtypes using deep learning on CT scans. Stud Health TechnolInformat 272:370–373

    Google Scholar 

  20. Karki M, Cho J, Lee E, Hahm MH, Yoon SY, Kim M, Ahn JY, Son J, Park SH, Kim KH, Park S (2020) CT window trainable neural network for improving intracranial hemorrhage detection by combining multiple settings. Artif Intell Med 101850.

Download references

Funding

The authors received no funding for this work.

Author information

Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt

    Romany F. Mansour

  2. College of Computer Science and Information Technology, Tabuk University, Tabuk, Saudi Arabia

    Nojood O. Aljehane

Authors
  1. Romany F. Mansour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nojood O. Aljehane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Romany F. Mansour.

Ethics declarations

Conflict of interests

The authors declare there are no conflict of interests.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mansour, R.F., Aljehane, N.O. An optimal segmentation with deep learning based inception network model for intracranial hemorrhage diagnosis. Neural Comput & Applic 33, 13831–13843 (2021). https://doi.org/10.1007/s00521-021-06020-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06020-8

Keywords

Search

Navigation