Transfer Learning-Based Approach for Early Detection of Alzheimer's Disease

Abstract

Alzheimer's disease is one of the world's main health concerns today. People with Alzheimer's disease who are diagnosed early have the best chance of receiving effective therapy. It's critical to catch the sickness as early as possible. Magnetic resonance imaging is one way to define Alzheimer's disease by finding structural abnormalities in the brain (MRI). We propose that machine learning, specifically trained convolutional neural networks (CNNs) with transfer learning capable of making predictions about similar brain imagery, can aid in early detection. CNN enables the extraction of MRI properties and classification as Alzheimer's disease or normal brain. We used the VGG19 architecture to categorize patients as having no signs of Alzheimer's disease or having signs of very mild, mild, or moderate Alzheimer's disease. Based on a transfer learning methodology, this method correctly classifies MRI images into four phases of Alzheimer's disease with an accuracy of 85 percent.

Multi Variate Feature Extraction and Feature Selection using LGKFS Algorithm for Detecting Alzheimer’s Disease

“…We also analyzed the performance of our multi variate feature extraction approach with deep learning approaches used earlier which is shown in Table 3. https://www.indjst.org/ (10) 3D dense net 121 87% Duc N T etal., (11) 3D CNN 85.27% Reddy G et al, (12) VGG 19 85% Proposed Multivariate feature extraction with LGKFS feature selection 90.8%…”

“…The authors have used 3D CNN to obtain the accuracy of 85.27% to distinguish AD from CN in (11) . Reddy G et al, have implemented VGG 19 and transfer learning to classify AD stages and achieved 85% accuracy in (12) Feature selection methods are the key component in AI assisted diagnosis. Several feature selection algorithms have been developed so far (13) .…”