Pathological Brain Tumour Detection Using Ridgelet Transform and SVM
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Publisher
Springer Singapore
Link
http://link.springer.com/content/pdf/10.1007/978-981-13-9184-2_11
Reference15 articles.
1. El-Sayed, A.E., Heba, M.M., Kenneth, R., Abdel-Badeeh, M.S.: Computer aided diagnosis of human brain tumour through MRI: a survey and a new algorithm. Expert Syst. Appl. 41(11), 5526–5545 (2014)
2. Jayachandran, A., Dhanasekaran, R.: Brain tumour severity analysis using modified multi-texton histogram and hybrid kernel SVM. Int. J. Imaging Syst. Technol. 24, 72–82 (2014)
3. Rufus, N.H.A., Selvathi, D.: Performance analysis of computer aided brain tumour detection system using ANFIS classifier. Int. J. Imaging Syst. Technol. 27(3), 273–280 (2017). https://doi.org/10.1002/ima.22232
4. Deepak, R.N., Ratnakar, D., Banshidhar, M.: Pathological brain detection using curvelet features and least squares SVM. Multimed. Tools Appl. 77(3), 3833–3856 (2016). https://doi.org/10.1007/s11042-016-4171-y
5. Anbarasa Pandian, A., Balasubramanian, R.: Fusion of contourlet transform and zernike moments using content based image retrieval for MRI brain tumour images. Indian J. Sci. Technol. 9(29), 0974–5645 (2016). https://doi.org/10.17485/ijst/2016/v9i29/93837
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