Measurement: Journal of the International Measurement Confederation, Volume 171, February 2021,
In the recent past, biomedical domain has become popular due to digital image processing of accurate and efficient diagnosis of clinical patients using Computer-Aided Diagnosis (CAD). Appropriate and punctual disease identification and treatment arrangement directs to enhance superiority of life and improved life hope in Alzheimer Disease (AD) patients. The cutting-edge approaches that believe multimodal analysis have been shown to be efficient and accurate are improved compared with manual analysis. Many tools have been introduced for detection of Alzheimer but still it is a financially high costly diagnosis system gives detection of disease with low accuracy and efficient due to performance of Magnetic Resonance Imaging (MRI) scanning devices. A novel methodology is proposed in this research as CAD process using various algorithms for predicting AD. The MRI images from scanning device are a highly noisy image due to thermal activities of hardware involved in scanning device. The image restoration technique is applied using 2D Adaptive Bilateral Filter (2D-ABF) algorithm. The quality of image in terms of brightness and contrast are improved using image enhancement techniques based on Adaptive Histogram Adjustment (AHA) algorithm. The Region of Interest of Alzheimer disease is segmented using Adaptive Mean Shift Modified Expectation Maximization (AMS-MEM) algorithm. The various features are calculated using second order 2-Dimensional Gray Level Co-Occurrence Matrix (2D-GLCM). Based on selection of features, the Deep Learning (DL) approach is used to classify the disease images and its stages. The Deep Convolutional Neural Network (DCNN) is the classification technique implemented to classify disease for proper diagnostic decision making. The experimental results prove that the proposed methodology provides better accuracy and efficiency than existing system.
2D-ABF; AHA; Adaptive Filters; Adaptive Mean Shifts; CAD; Classification Technique; Computer Aided Diagnosis; Computer Aided Diagnosis Systems; Computer Aided Diagnosis(CAD); Convolution; Convolutional Neural Networks; Decision Making; Deep Learning; Deep Neural Networks; Diagnostic Decision Makings; GLCM; Gray Level Co-occurrence Matrix; Image Enhancement; Image Reconstruction; Image Restoration Techniques; Image Segmentation; ML; MRI; Magnetic Resonance Imaging; Maximum Principle; Medical Computing; Modal Analysis; Modified Expectation-maximization; Neurodegenerative Diseases; Patient Treatment; Profilometry; Scanning; Global