Skip to main content
SpringerLink
Log in

A Research Study on Brain Tumor Detection Techniques

  • Conference paper
  • First Online:
Proceedings of International Conference on Communication and Artificial Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 435))

  • 641 Accesses

Abstract

Even though there are remarkable advancements in brain tumor detection in medical technology, it remains the most tedious and complicated issue for doctors. For brain tumor detection, radiologists commonly use Magnetic Resonance Imaging (MRI). The MRI provided both the standard and abnormal anatomy of the brain. Also, MRI does not require dissection. Computer-aided image analysis is the promising solution for detecting the diseases such as a tumor, cancer earlier stages. Automatic computerized classification for detecting the tumor from the MRI image is essential. It will help detect the tumor, reduce the number of hours worked, lessen errors, and help decide the treatment plan. This work depicts the techniques proposed in contemporary literature by briefing the novel facts of the research.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Nassiri MJ, Vafaei A, Monadjemi A (2007) Texture feature extraction using Slant-Hadamard transform. Int J Appl Sci Eng Technol 3:4

    Google Scholar 

  2. Zhang Y, Wang S, Sun P, Phillips P (2015) Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-med Mater Eng 26(s1):S1283–S1290

    Article  Google Scholar 

  3. Wang S, Zhang Y, Yang X, Sun P, Dong Z, Liu A et al (2015) Pathological brain detection by a novel image feature-fractional Fourier entropy. Entropy 17(12):8278–8296

    Article  Google Scholar 

  4. Zhou X, Wang S, Xu W, Ji G, Phillips P, Sun P et al (2015) Detection of pathological brain in MRI scanning based on wavelet-entropy and naive Bayes classifier. In: International conference on bioinformatics and biomedical engineering, pp 201–209

    Google Scholar 

  5. Laishram R, Kumar W K, Gupta A, Prakash KV (2014) A novel MRI brain edge detection using PSOFCM segmentation and canny algorithm. In: 2014 International conference on electronic systems, signal processing and computing technologies (ICESC), pp 398–401

    Google Scholar 

  6. Oo SZ, Khaing AS (2014) Brain tumor detection and segmentation using watershed segmentation and morphological operation. IJRET Int J Res Eng Technol 3(3):367–374

    Article  Google Scholar 

  7. Ibrahim WH, Osman AAA, Mohamed YI (2013) MRI brain image classification using neural networks. In: 2013 International conference on computing, electrical, and electronics engineering (ICCEEE), pp 253–258

    Google Scholar 

  8. Islam A, Reza SM, Iftekharuddin KM (2013) Multifractal texture estimation for detection and segmentation of brain tumors. IEEE Trans Biomed Eng 60(11):3204–3215

    Article  Google Scholar 

  9. Sivaramakrishnan A, Karnan DM (2013) A novel based approach for extraction of brain tumor in MRI images using soft computing techniques. Int J Adv Res Comput Commun Eng 1845–1848. ISSN, No. 2319-5940

    Google Scholar 

  10. Ali S, Abood LK, Abdoon RS (2013) Brain tumor extraction in MRI images using clustering and morphological operations techniques. Int J Geograph Inf Syst Appl Remote Sens 4(1):12–25

    Google Scholar 

  11. Patil SB (2016) Detection of brain tumor based on segmentation using region growing method. Int J Eng Innov Res 5(2):173–176

    Google Scholar 

  12. Machhale K, Nandpuru HB et al (2015) MRI brain cancer classification using hybrid classifier (SVM-KNN). In: International conference on industrial instrumentation and control (ICIC). IEEE, pp 60–65. 978-1-4799-7165-7/15

    Google Scholar 

  13. Xiao K, Lei Liang A, Guan HB, Hassanien AE (2013) Extraction and application of deformation based feature in medical images. Neurocomputing

    Google Scholar 

  14. Gopal PN, Sukanesh R (2013) Wavelet statistical feature based segmentation and classification of brain computed tomography images. IET Image Process 7:25–32

    Google Scholar 

  15. Kalbkhani H, Shayesteh MG, Zali-Vargahan B (2013) Robust algorithm for brain magnetic resonance image classification based on GARCH variances series. Biomed Signal Process Control 8:909–919

    Google Scholar 

  16. Sindhumol S, Kumar A, Balakrishnan K (2013) Spectral clustering independent component analysis for tissue classification from brain MRI. Biomed Signal Process Control 667–674

    Google Scholar 

  17. Navarro DH et al (2015) Feature and model selection with discriminatory visualization for diagnostic classification of brain tumor. Neurocomputing 73(4–6):622–632

    Google Scholar 

  18. Sumitra N, Saxena RK (2013) Brain tumor classification using back propagation neural network. Int J Image Graph Signal Process 45–50

    Google Scholar 

  19. Jayachandran A, Dhanasekaran R (2013) Brain tumor detection and classification of MRI using texture feature and fuzzy SVM classifiers. Res J Appl Sci Eng Tech 6(12):2264–2269

    Google Scholar 

  20. Padma Nanthagopal A, Sukanesh Rajamony R (2013) Classification of benign and malignant brain tumor CT images using wavelet texture parameters and neural network classifiers. J Vis 16:19–28

    Google Scholar 

  21. Shree NV, Kumar TNR (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inf 5(1):23–30

    Google Scholar 

  22. Mohsen H, El-Dahshan E-SA, El-Horbaty E-SM, Salem A-BM (2018) Classification using deep learning neural networks for brain tumors. Future Comput Inf J 3(1):68–71

    Google Scholar 

  23. Mathew AR, Babu Anto P (2017) Tumor detection and classification of MRI brain image using wavelet transform and SVM. In: 2017 International conference on signal processing and communication (ICSPC). IEEE, pp 75–78

    Google Scholar 

  24. Shil SK, Polly FP, Hossain MA, Shareef Ifthekhar Md, Uddin MN, Jang YM (2017) An improved brain tumor detection and classification mechanism. In: 2017 International conference on information and communication technology convergence (ICTC). IEEE, pp 54–57

    Google Scholar 

  25. Usman K, Rajpoot K (2017) Brain tumor classification from multi-modality MRI using wavelets and machine learning. Pattern Anal Appl 20(3):871–881. https://doi.org/10.1007/s10044-017-0597-8

  26. Amin J, Sharif M, Yasmin M, Fernandes SL (2017) A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn Lett. https://doi.org/10.1016/j.patrec.2017.10.036

  27. Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, Wang Z, Feng Q (2015) Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS One 10(10):e0140381

    Google Scholar 

  28. Kermi A, Andjouh K, Zidane F (2018) Fully automated brain tumor segmentation system in 3D-MRI using symmetry analysis of brain and level sets. IET Image Process 12(11):1964–1971

    Google Scholar 

  29. Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imaging

    Google Scholar 

  30. Praveen GB, Agrawal A (2015) Hybrid approach for brain tumor detection and classification in magnetic resonance images. In: Communication, control and intelligent systems (CCIS). IEEE, pp 162–166

    Google Scholar 

  31. Kaur T, Saini BS, Gupta S (2017) Quantitative metric for MR brain tumor grade classification using sample space density measure of analytic intrinsic mode function representation. IET Image Process 11(8):620–632

    Google Scholar 

  32. Sornam M, Kavitha MS, Shalini R (2016) Segmentation and classification of brain tumor using wavelet and Zernike based features on MRI. In: IEEE International conference on advances in computer applications (ICACA). IEEE, pp 166–169

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India

    Nisha Joseph & D. Murugan

  2. Department of Computer Science and Engineering, Satyabhama University, Chennai, Tamil Nadu, India

    Divya Mohan

Authors
  1. Nisha Joseph
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Murugan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Divya Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nisha Joseph .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, GLA University, Mathura, India

    Vishal Goyal

  2. Department of Electronics and Communication Engineering, GLA University, Mathura, India

    Manish Gupta

  3. Torrens University Australia, Adelaide, SA, Australia

    Seyedali Mirjalili

  4. Atal Bihari Vajpayee-Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India

    Aditya Trivedi

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Joseph, N., Murugan, D., Mohan, D. (2022). A Research Study on Brain Tumor Detection Techniques. In: Goyal, V., Gupta, M., Mirjalili, S., Trivedi, A. (eds) Proceedings of International Conference on Communication and Artificial Intelligence. Lecture Notes in Networks and Systems, vol 435. Springer, Singapore. https://doi.org/10.1007/978-981-19-0976-4_43

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-0976-4_43

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0975-7

  • Online ISBN: 978-981-19-0976-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation