Skip to main content
SpringerLink
Log in

RE-InCep-BT-:Resource-Efficient InCeptor Model for Brain Tumor Diagnostic Healthcare Applications in Computer Vision

  • Research
  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

The rising incidence of brain tumors in the medical field necessitates the development of precise and effective diagnostic tools to assist the medical experts especially neurosurgeons as well as radiologists in early diagnosis and treatment recommendations. This study introduces a unique resource-efficient inceptor model utilizing computer-vision techniques for diagnosing presence of abnormal tissues inside brain MRI scans. The proposed model utilizes strengths of the inception architecture and incorporate resource-efficient design principles for optimizing its performance for healthcare applications. The model has been trained on a distinct dataset with different sizes where it is further processed, trained and validated on InCeptor model. Features are extracted by transfer learning process namely InceptionV3 for leveraging prior knowledge learnt from imagenet which is further integrated with support vector machines for performing binary classification to have accurate and efficient outcomes for giving timely recommendation and treatment to patients suffering from such disorder. The architecture of the proposed model has been designed in such a way that model should be computationally efficient for making it suitable in healthcare especially for brain tumor diagnostic purpose with limited resources. Experimental results demonstrates accuracy of 98.31%, precision of 99.09%, recall of 98.91%, specificity of 95% and F1- Score of 99% over state of art techniques.

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
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability statement

No datasets were generated or analysed during the current study.

References

  1. Louis DN, Perry A, Reifenberger G, Von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820

    Article  Google Scholar 

  2. Brain Tumor Facts, National Brain Tumor Society, https://braintumor.org/brain-tumors/about-brain-tumors/brain-tumor-facts/

  3. Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB (2018) Deep learning mr imaging-based attenuation correction for pet/mr imaging. Radiology 286(2):676–684

    Article  Google Scholar 

  4. Fernando T, Gammulle H, Denman S, Sridharan S, Fookes C (2021) Deep learning for medical anomaly detection-a survey. ACM Computing Surveys (CSUR) 54(7):1–37

    Article  Google Scholar 

  5. Huang Z, Du X, Chen L, Li Y, Liu M, Chou Y, Jin L (2020) Convolutional neural network based on complex networks for brain tumor image classification with a modified activation function. IEEE Access 8:89281–89290

    Article  Google Scholar 

  6. Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J (2019) Brain tumor classification for mr images using transfer learning and fine-tuning. Comput Med Imaging Graph 75:34–46

    Article  Google Scholar 

  7. Salçin K et al (2019) Detection and classification of brain tumours from mri images using faster r-cnn. Tehnički glasnik 13(4):337–342

    Article  Google Scholar 

  8. Sarkar S, Kumar A, Chakraborty S, Aich S, Sim J-S, Kim H-C (2020) A cnn based approach for the detection of brain tumor using mri scans. Test Eng Manag 83:16580–16586

    Google Scholar 

  9. Rehman A, Khan MA, Saba T, Mehmood Z, Tariq U, Ayesha N (2021) Microscopic brain tumor detection and classification using 3d cnn and feature selection architecture. Microsc Res Tech 84(1):133–149

    Article  Google Scholar 

  10. Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW (2019) Multi-grade brain tumor classification using deep cnn with extensive data augmentation. J Comput Sci 30:174–182

    Article  Google Scholar 

  11. Shanthakumar P, Ganesh Kumar P (2015) Computer aided brain tumor detection system using watershed segmentation techniques. Int J Imaging Syst Technol 25(4):297–301

    Article  Google Scholar 

  12. Thejaswini P, Bhat MB, Prakash MK (2019) Detection and classification of tumour in brain mri. Int. J. Eng. Manufact. (IJEM) 9(1):11–20

    Article  Google Scholar 

  13. Keerthana T, Xavier S (2018) An intelligent system for early assessment and classification of brain tumor. In: 2018 Second international conference on inventive communication and computational technologies (ICICCT), IEEE, pp 1265–1268

  14. Prastawa M, Bullitt E, Ho S, Gerig G (2004) A brain tumor segmentation framework based on outlier detection. Med Image Anal 8(3):275–283

    Article  Google Scholar 

  15. Gumaei A, Hassan MM, Hassan MR, Alelaiwi A, Fortino G (2019) A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access 7:36266–36273

    Article  Google Scholar 

  16. Muis A, Sunardi S, Yudhana A (2024) Cnn-based approach for enhancing brain tumor image classification accuracy. Int J Eng 37(5):984–996

    Article  Google Scholar 

  17. Agrawal T, Choudhary P, Shankar A, Singh P, Diwakar M (2024) Multifenet: Multi-scale feature scaling in deep neural network for the brain tumour classification in mri images. Int J Imaging Syst Technol 34(1):22956

    Article  Google Scholar 

  18. Mahmud MI, Mamun M, Abdelgawad A (2023) A deep analysis of brain tumor detection from mr images using deep learning networks. Algorithms 16(4):176

    Article  Google Scholar 

  19. Chaudhary A, Hazra A, Chaudhary P (2019) Diagnosis of chest diseases in x-ray images using deep convolutional neural network. In: 2019 10th International conference on computing, communication and networking technologies (ICCCNT), IEEE, pp 1–6

  20. Choudhary P, Hazra A (2021) Chest disease radiography in twofold: using convolutional neural networks and transfer learning. Evol Syst 12(2):567–579

    Article  Google Scholar 

  21. Saeedi S, Rezayi S, Keshavarz HR, Niakan Kalhori S (2023) Mri-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Med Inform Decis Mak 23(1):16

  22. Sharma AK, Nandal A, Dhaka A, Koundal D, Bogatinoska DC, Alyami H et al (2022) Enhanced watershed segmentation algorithm-based modified resnet50 model for brain tumor detection. BioMed Res Int 2022

  23. Brain Tumor Classification from MRI Images, https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri

  24. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  25. Tang Y (2013) Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239

  26. Thapa K, Khan H, Singh TG, Kaur A (2021) Traumatic brain injury: mechanistic insight on pathophysiology and potential therapeutic targets. J Mol Neurosci 71(9):1725–1742

    Article  Google Scholar 

  27. Rehni AK, Singh TG, Jaggi AS, Singh N (2008) Pharmacological preconditioning of the brain: a possible interplay between opioid and calcitonin gene related peptide transduction systems. Pharmacol Rep 60(6):904

    Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India

    Kamini Lamba & Shalli Rani

  2. Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon

    Muhammad Attique Khan

  3. Model Institute of Engineering and Technology, Jammu, J &K, India

    Mohammad Shabaz

Authors
  1. Kamini Lamba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shalli Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Attique Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammad Shabaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Main draft has been prepared by Kamini Lamba and Shalli Rani. Software and resources are handled by Muhammad Attique Khan and Mohammad Shabaz. All the authors contributed equally to the manuscript.

Corresponding author

Correspondence to Shalli Rani.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

The authors declare no conflict of interest.

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

Lamba, K., Rani, S., Khan, M.A. et al. RE-InCep-BT-:Resource-Efficient InCeptor Model for Brain Tumor Diagnostic Healthcare Applications in Computer Vision. Mobile Netw Appl (2024). https://doi.org/10.1007/s11036-024-02320-0

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11036-024-02320-0

Keywords

Search

Navigation