R. K. Agrawal

Magnetic Resonance Imaging,Image Segmentation,Alzheimer’s Disease,Alzheimer’s Disease Neuroimaging Initiative,Brain Magnetic Resonance Imaging,Convolutional Layers,Convolutional Neural Network,Filter Size,Fine Details,Fuzzy C-means Clustering,Fuzzy Set,Intuitionistic Fuzzy,Mild Cognitive Impairment,Negation,Number Of Filters,Performance Of Method,Segmentation Method,Spatial Information,Support Vector Machine,Activation Function,Adjacent Slices,AlexNet,Autocorrelation Matrix,Average Accuracy,Bhattacharyya Distance,Bias Correction,Brain Tissue,Brain-computer Interface System,Class Labels,Classical Learning Algorithms,Classification Of Cognitive Impairment,Classification Of Impairment,Cluster Centroids,Clustering Method,Combination Of Selection,Computational Complexity,Deep Learning,Dice Score,Different Levels Of Noise,Different Types Of Datasets,Dimensionality Reduction,EEG Signals,Empirical Mode Decomposition,Evolutionary Algorithms,Feature Extraction Methods,Feature Ranking,Feature Reduction,Feature Selection Approach,Feature Selection Methods,Feature Selection Techniques,