Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

By S. Chaplot, Lalit M. Patnaik, N. Jagannathan. Biomedical Signal Processing and Control, 2006

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Automated medical image segmentation techniques (2010, methodology): “Some methods available for CT image segmentation are: threshold based[54] region based[55] deformable models based[56-57] fuzzy based[58,59] neural network based[45-47,60]”
Accurate brain tumor detection using deep convolutional neural network (2022, methodology): “4743 To classify the binary class, previous studies used an imbalance dataset [9][27][28].”
Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm (2014, methodology): “…et al., 2006) 96 DWT + SVM with radial basis function based kernel (Dubey et al., 2009; Chaplot et al., 2006) 98 DWT + SOM (Dubey et al., 2009; Chaplot et al., 2006) 94 PCA + SVM (Wang & Fei, 2009; Zöllner, 2012) 85 89 84 ICA + SVM (Wang & Fei, 2009; Zöllner, 2012) 79 87 75 PCC + SVM (Wang &…”
A hybrid method for MRI brain image classification (2011, background): “The other category is unsupervised classification, such as self-organization feature map (SOFM) (Chaplot et al., 2006) and fuzzy c-means (Maitra & Chatterjee, 2008).”