Skip to main content
SpringerLink
Log in

RETRACTED ARTICLE: Computer aided diagnosis of brain tumor using novel classification techniques

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

This article was retracted on 15 June 2022

This article has been updated

Abstract

Brain cancer treatment mainly depends on the accurate detection of the tumor type, location, size and borders. Magnetic resonance images (MRI) can be used to analyze the properties of the desired region such as tissues and tumors with automated and semi-automated approaches. So, the extraction of MRI brain tumor image is a challenging task in medical image processing. The major problems associated with MRI analysis by a physician are time consuming and the accuracy depends on the expertise of the physician.This limitation can be overcome by the computer aided diagnosis (CAD) technology. In this paper, a CAD system is designed to detect brain tumors with computer assistance using T1 and T2 weighted MR images. The designed system classifies the tumor into benign or malignant from MR Image using a novel automated method which increases the performance and reduces the complexity involved in the tumor diagnosis. The CAD system has four stages such as image acquisition, segmentation, feature extraction and classification. Segmentation is done with the help of K-means clustering, which enhances the medical image and the clustering quality to avoid local optima and to find global optima. The feature extraction is performed by gray level co-occurrence matrix (GLCM). The tumor classification is done using support vector machine (SVM) and bag of visual words (BOVW) classifiers. The test result of the SVM classifier gives accuracy 95.0%, sensitivity 91.79% and specificity 94.75%. Whereas, the BOVW classifier yields results of accuracy 96.0%, sensitivity 90.0% and specificity 100%. This result shows that, the designed system classifies the tumor into benign or malignant with a good level of accuracy.

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

Similar content being viewed by others

Change history

References

  • Akram UM, Anam U (2011) Computer aided system for brain tumor detection and segmentation. In: 2011 international conference on computer networks and information technology (ICCNIT). IEEE, pp 299–302

  • Al-Kadi OS (2015) A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours. Comput Med Imaging Graph 41:67–79

    Article  Google Scholar 

  • Amin J, Sharif M, Raza M, Yasmin M (2018) Detection of brain tumor based on features fusion and machine learning. J Ambient Intell Humaniz Comput 1–17

  • Bahadure NB, Arun KR, Har PT (2017) Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imaging

  • Demirhan A, Mustafa T, Inan G (2015) Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE J Biomed Health Inform 19(4):1451–1458

    Article  Google Scholar 

  • Desprechins B, Tadeusz S, Guus K, Wael S, Catherine B, Michel O (1999) Use of diffusion-weighted MR imaging in differential diagnosis between intracerebral necrotic tumors and cerebral abscesses. Am J Neuroradiol 20(7):1252–1257

    Google Scholar 

  • Dhage P, Phegade MR, Shah SK (2015) Watershed segmentation brain tumor detection. In: 2015 international conference on pervasive computing (ICPC). IEEE, pp 1–5

  • Dhanachandra N, Khumanthem M, Yambem JC (2015) Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Proc Comput Sci 54:764–771

    Article  Google Scholar 

  • Dhanwani DC, Mahip MB (2014) Survey on various techniques of brain tumor detection from MRI images. Int J Comput Eng Res 4(1):24–26

    Google Scholar 

  • El-Dahshan E-S, Tamer H, Abdel-Badeeh M (2010) Hybrid intelligent techniques for MRI brain images classification. Dig Signal Process 20(2):433–441

    Article  Google Scholar 

  • Jain S (2013) Brain cancer classification using GLCM based feature extraction in artificial neural network. Int J Comput Sci Eng Technol 4(7):966–970

    Google Scholar 

  • Kumar SB, Anbuselvi N (2015) A hybrid approach for classification of brain tumors. Int J Comput Syst (ISSN: 2394–1065) 02

  • Lal S, Mahesh C (2014) Efficient algorithm for contrast enhancement of natural images. Int Arab J Inf Technol 11(1):95–102

    Google Scholar 

  • Ramadevi ST, Poornima B, Kalyani B (2010) Segmentation and object recognition using edge detection techniques. Int J Comput Sci Inf Technol (IJCSIT) 2(6):153–161

    Google Scholar 

  • Ratan R, Sanjay S, Sharma SK (2009) Brain tumor detection based on multi-parameter MRI image analysis. ICGST-GVIP J 9(3):9–17

    Google Scholar 

  • Sahu G, Lalitkumar PB (2015) Classification of MRI brain images using GLCM, neural network, fuzzy logic & genetic algorithm. Int J Recent Innov Trends Comput Commun 3(6):3498–3504

    Google Scholar 

  • Selvaraj H, ThamaraiSelvi S, Selvathi D, Gewali L (2007) Brain MRI slices classification using least squares support vector machine. Int J Intell Comput Med Sci Image Process 1(1):21–33

    Google Scholar 

  • Sharif M, Tanvir U, Munir EU, Khan MA, Yasmin M (2018) Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection. J Ambient Intell Humaniz Comput 1–20

  • Soltanian-Zadeh H, Joe PW, Donald JP (1996) Optimal linear transformation for MRI feature extraction. IEEE Trans Med Imaging 15(6):749–767

    Article  Google Scholar 

  • Van G, Robert-Jan M, Piotr AW, Hein GB, Benno RJ, Peter O, Marc H, Matthijs O, Pim JF (1999) Basic principles of magnetic resonance imaging. Prog Cardiovasc Dis 42(2):149–156

    Article  Google Scholar 

  • Vijay J, Subhashini J (2013) An efficient brain tumor detection methodology using K-means clustering algoriftnn. In: 2013 international conference on communications and signal processing (ICCSP). IEEE, pp 653–657

  • Zhang Y, Zhengchao D, Lenan W, Shuihua W (2011) A hybrid method for MRI brain image classification. Expert Syst Appl 38(8):10049–10053

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, Marthandam College of Engineering and Technology, Kuttakuzhi, TamilNadu, India

    Jasmine Paul

  2. Department of EEE, Arunachala College of Engineering for Women, Vellichanthai, TamilNadu, India

    T. S. Sivarani

Authors
  1. Jasmine Paul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. S. Sivarani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jasmine Paul.

Additional information

Publisher's Note

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

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04137-9

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paul, J., Sivarani, T.S. RETRACTED ARTICLE: Computer aided diagnosis of brain tumor using novel classification techniques. J Ambient Intell Human Comput 12, 7499–7509 (2021). https://doi.org/10.1007/s12652-020-02429-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02429-6

Keywords

Search

Navigation