PlumX Metrics
Embed PlumX Metrics

Gauss-Newton Representation Based Algorithm for Magnetic Resonance Brain Image Classification

Advances in Intelligent Systems and Computing, ISSN: 2194-5357, Vol: 736, Page: 294-304
2018
  • 1
    Citations
  • 0
    Usage
  • 3
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

Conference Paper Description

Brain tumor is a harmful disease worldwide. Every year, a majority of adults as well as children dies due to brain tumor. Early detection of the tumor can enhance the survival rate. Many brain image classification schemes are reported in the literature for early detection of tumors. Thus, it has become a challenging problem in the field of medical image analysis. In this paper, a novel hybrid method is proposed that uses the Gauss-Newton representation based algorithm (GNRBA) with feature selection approach. The proposed method is threefold. Firstly, discrete wavelet transform (DWT) is used as a pre-processing step to extract the features from the brain images. Secondly, principal component analysis (PCA) is used to address the dimensionality problem. Finally, the extracted features in the lower dimensional space are utilized by GNRBA for classification. To show the robustness of the proposed method, real human brain magnetic resonance (MR) images are used to experiment. It is witnessed from the results that the performance of the proposed method is superior as compared to the existing brain image classification methods.

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know