… This paper experiments with ten popular classification … support vector machines to speed
up the computation process. Numerical experiments show that support vector machines and …

… a multi-modal MRI brain tumor image segmentation network, named D3D-… segmenting brain
tumors from MRI images. The network can improve the accuracy of brain tumor segmentation…

… To address these challenges, we proposed a deep learning-based approach for automated
segmentation and quantification of CCM on T2W. Firstly, a Mask Region based Convolution. …

… The other is correcting the images. In this study, we used the dynamic clustering algorithm
for integration pattern recognition that could support vector machine theory. We also used an …

… Machine learning models have demonstrated remarkable accuracy in imaging analysis,
diagnosing stroke subtypes, risk stratifications, guiding medical treatment, and predicting patient …

… In general, we need to transform the multispectral images to an enhanced image, which is
… is the brain parenchyma classification. In this dissertation, we present an automatic feature …

… An adaptive fuzzy K-nearest neighbor approach for MR brain tumor image classification
using parameter free bat optimization algorithm. Multimedia Tools and Applications, 78(15): …

… brain tumor grading model (BTGM) composed of a brain tumor segmentation model, a
brain tumor classification … 支持向量机(support vector machine,SVM)结合的脑肿瘤分割方法[8-9],基 …

… Two multi-classification strategies used on SVM to predict protein structural classes by
using auto … feature selection and fuzzy LS-SVM-based classifier in motion classification. …

… assessment of glioma burden: a deep learning algorithm for fully automated … support vector
machine (SVM) exhibited superior performance to other classifiers. By combining all tumor …