Alzheimer Disease Detection And Classification On Magnetic Resonance Imaging (Mri) Brain Images Using Improved Expectation Maximization (Iem) And Convolutional Neural Network (Cnn)

Abstract

: In the recent past, the Computer Aided Tomography (CAD) has become significant automation tool for efficient and accurate medical diagnosis based on images captured by the medical scanning devices. In Magnetic Resonance (MR) brain image processing, clustering and segmentation are mainly applied for identifying, computing and investigating the significant functional arrangements of the human brain and ultimately detecting physical pathological area. NRI brain image clustering and segmentation are important since that supports physicians and research academician to focus on precise areas of the human brain in direction to investigate it. The Alzheimer Disease (AD) is the one of the human brain disease that is suspect to adapt its connected and inherent decline, initial analysis is essential, that provides human a alteration to reorganize their survives. Brain image clustering and segmentation are significant feature of medical analytical tools, resilient outstanding outcomes associated to existing clustering and segmentation methods. In this research, novel methodology is proposed for efficient and accurate segmentation of AD disease region. The MRI brain image may contain various noises such as salt and pepper noise, Gaussian noise, speckle noise and random noise due to scanning devices while capturing brain texture. The 2D-Adaptive Consensual Filter (2D-ACF) is proposed for eliminating all types of noises occurred in the MRI images. The Edge-Preservation Coherence Improvement (EP-CI) algorithm is proposed to improve contrast and brightness in order to improve the quality of the image. The Efficient Fuzzy C Means Adaptive Thresholding (EFCMAT) algorithm is used to segment the AD region from MRI image. The experimental results show that the proposed methodology provides better results than existing methodologies.