Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer’s Disease Detection

Abstract

Alzheimer’s disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning. This article presents an Alzheimer’s disease detection framework consisting of image denoising of an MRI input data set using an adaptive mean filter, preprocessing using histogram equalization, and feature extraction by Haar wavelet transform. Classification is performed using LS-SVM-RBF, SVM, KNN, and random forest classifier. An adaptive mean filter removes noise from the existing MRI images. Image quality is enhanced by histogram equalization. Experimental results are compared using parameters such as accuracy, sensitivity, specificity, precision, and recall.

DeepCurvMRI: Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimer’s Disease

“…A series of feature preprocessing algorithms combined with the classifier is necessary for improving the classifier accuracy. For example, Kamal et al [26] preprocessed MRI images using an adaptive mean filter and histogram equalization. Afterwards, image features were extracted using Haar Transform for the binary classification of AD using SVM.…”

“…WT can detect features overlooked by other feature extraction methods, such as breakdown points and discontinuities. Several other studies have also utilized WT as a tool for feature extraction in the form of wavelet coefficients from MRI images [26], [39], [40], [41]. However, WT's major limitation is its inability to identify curved edges, which in some cases causes false alarms.…”

Retracted: Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer’s Disease Detection

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”