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M.R. brain tumor classification employing ICA and kernel-based support vector machine

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Abstract

A revolutionary and robust magnetic resonance (MR) brain tumor detection and segmentation approach has been presented in this work. We have put forward a robust technique to extract the tumor and classify the same as benign or malignant. The extraction of features from detected lesions is achieved through usage of wavelet transform. There is an immediate need to eliminate the redundant features from extracted subset because they degrade the performance of classification. Now, these reduced features are fed to kernel-based SVM (K-SVM). The kernel that is being utilized in this framework is Gaussian radial basis as it is quite efficient. Once the MR images are classified as benign or malignant, the next obvious step is to segment out the infected portion. We have relied upon region growing technique for the segmentation of the infected area. K-fold cross-validation approach has been adopted to optimize the performance K-SVM. We have compared the outcomes of our approach to that of various in-class conventional approaches. Experimental outcomes clearly suggest that our approach has performed efficiently and robustly for almost entire set of data and has performed way better when compared to existing in-class methodologies both qualitatively as well as quantitatively.

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Not applicable. The magnetic resonance images used in this study are public databases that are cited within the text

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Acknowledgements

The authors are very thankful to the editor and the anonymous reviewers for their valuable comments and suggestions.

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  1. Department of Electronics and Communication, Maulana Azad National Institute of Technology, Bhopal, M.P., India

    Rahul Singh, Aditya Goel & D. K. Raghuvanshi

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  1. Rahul Singh
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  2. Aditya Goel
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  3. D. K. Raghuvanshi
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Correspondence to Rahul Singh.

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Singh, R., Goel, A. & Raghuvanshi, D.K. M.R. brain tumor classification employing ICA and kernel-based support vector machine. SIViP 15, 501–510 (2021). https://doi.org/10.1007/s11760-020-01770-9

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