Big Data Analytics for Brain Tumour Detection Using Subspace Clustering

Authors

  • Padmavathi Vanka Research Scholar, Department of Computer Science Sri Padmavathi Mahila Viswa Vidyalayam, Tirupati, Andhra Pradesh, India
  • T. Sudha Professor, Department of Computer Science Sri Padmavathi Mahila Viswa Vidyalayam, Tirupati, Andhra Pradesh, India

Keywords:

Subspace Clustering, Slicer Tool, CLIQUE

Abstract

In healthcare industry data mining holds a great prospective to enable health systems steadily use big data and analytics to identify inefficiencies which improves care and reduces cost. Due to the slower rate of technological adoption in health care, software industry lags in implementing effective data mining and analytic strategies. The segmentation, detection, and extraction of infected area of tumours from magnetic resonance images (MRI) are a primary distress but a tiresome task performed by radiologists or clinical experts. To improve the performance and reduce the complexities in segmenting the MRI data new methods need to be introduced. A 3D magnetic resonance images can give better accuracy in detecting the tumour area. In this paper we proposed subspace clustering techniques to detect brain tumours using 3D images.

References

V. Janani, and P. Meena, “Image segmentation for tumour detection using fuzzy inference system”, International Journal of Computer Science and Mobile Computing (IJCSMC), Vol. 02, No. pp. 244–248, 2013.

J. Patel and K. Doshi “A study of segmentation methods for detection of tumour in brain MRI”, International Journal of advanced electronics and electrical engineering, Vol. 04, No. pp. 279–284, 2014.

M. Rohit, S. Kabade and M.S. Gaikwad, “Segmentation of brain tumour and its area calculation in brain MRI images using K-mean clustering and Fuzzy C-mean algorithm”, International Journal of Computer Science Engineering and Technology (IJCSET), Vol. 04, No. pp.524–531, 2013.

H.A. Aslam, T. Ramashri, and M.I.A. Ahsan, “A new approach to image segmentation for brain tumour detection using pillar K-meansalgorithm”, International Journal of Advanced Research Compution and Communication Engineering, Vol. 2, pp. 1429–1436, 2013.

Priyanka Shah, Manila Jeshnani, SagarKukreja and PriyankaAilani, “Survey on Algorithms for Brain Tumour Detection”, International Journal of Computer Science and Information Technologies, Vol. 08, No. pp. 56–58, 2017.

NileshBhaskarraoBahadure, Arun Kumar Ray and Har Pal Thethi, “Image Analysis for MRI Based Brain Tumour Detection and Feature Extraction Using Biologically Inspired BWT and SVM”, International Journal of Biomedical Imaging, Vol. 2017, Mar. 2017.

S. Damodharan and D. Raghavan, “Combining tissue segmentation and neural network for brain tumour detection”, InternationalArab Journal of Information Technology, Vol. 12, No. pp. 42–52, 2015.

E. AbdelMaksoud, M. Elmogy, and R. Al-Awadi, “Brain tumour segmentation based on a hybrid clustering technique,” Egyptian Informatics Journal, Vol. 16, No. pp. 71–81, 2014.

B. Ramish. Kawadiwale and Milind E. Rane, “Clustering Techniques for Brain Tumour Detection”, Association of Computer Electronics and Electrical Engineers, 2014.

ShravanRao, Meet Parikh, Mohit Parikh and Chinmay Nemade “Implementation of Clustering Techniques For Brain Tumour Detection”, International Research Journal of Engineering and Technology, Vol. 03. No. pp. 517–521, Apr. 2016.

Lance Parsons, Ehtesham Haque, and Huan Liu “Subspace Clustering for High Dimensional Data: A Review”, Sigkdd Explorations, Vol. 6, pp.90–105, 2004.

R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghava, “Automatic subspace clustering of high dimensional data for data mining applications”, In Proceedings of the 1998 ACM SIGMOD International conference on Management of data, ACM Press, pp. 94–105, 1998.

C.H. Cheng, A.W. Fu, and Y. Zhang, “Entropy-based subspace clustering for mining numerical data”, In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM Press, pp. 84–93, 1999.

Published

15-11-2019

How to Cite

Vanka, P., & Sudha, T. (2019). Big Data Analytics for Brain Tumour Detection Using Subspace Clustering. Asian Journal of Computer Science and Technology, 8(3), 23–26. Retrieved from https://ojs.trp.org.in/index.php/ajcst/article/view/2736