Big Data Analytics for Brain Tumour Detection Using Subspace Clustering


  • 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


Subspace Clustering, Slicer Tool, CLIQUE


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.


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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