Research Article Open Access

Whale Optimized Deep Generative Adversarial Network Based Alzheimer's Stages Detection Using 3D MRI Brain Neuroimaging

R. Sampath1, R. Sampath2 and M. Baskar 3
  • 1 Department of Computer Science and Engineering, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nādu, 603203, India
  • 2 Department of Information Technology, Sri Sairam Institute of Technology Chennai, Tamil Nadu, 600044, India
  • 3 Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nādu, 603203, India

Abstract

Alzheimer's Disease (AD), a common, chronic neurodegenerative condition, is characterized by the loss of neurons and synapses in the cerebral cortex and specific subcortical regions. According to claims from a recent study, AD has a 20% misdiagnosis rate. Therefore, it is essential to create a useful tool to recognize the stages of AD with a lower prediction error rate to reduce misdiagnosis. Hence proposed a model called Whale-Optimized Deep Generative Adversarial Network (WODGAN). A generator plus a discriminator make up the model. The discriminator trains the model using real images; The generator creates synthetic images using noise and random selection. The discriminator goes through some processes to improve image quality, including Adaptive Histogram Equalization (AHE) and Adaptive Filtering (AF) approaches. Fuzzy feature extraction techniques are used to accurately segregate biomarker regions from brain MRI scans depending on AD pathology. The model uses Hilbert-Schmidt Independence Criterion Lasso (HSICL) to discover optimized biomarker features to combat overfitting. Before training, the discriminator can tell actual photos from artificial ones. The Whale Optimizer (WO) is used during training to improve network efficiency and lower prediction errors. The numerical results show a high accuracy of 99.93% in AD stage recognition.

Journal of Computer Science
Volume 19 No. 8, 2023, 998-1014

DOI: https://doi.org/10.3844/jcssp.2023.998.1014

Submitted On: 24 April 2023 Published On: 4 August 2023

How to Cite: Sampath, R. & Baskar , M. (2023). Whale Optimized Deep Generative Adversarial Network Based Alzheimer's Stages Detection Using 3D MRI Brain Neuroimaging. Journal of Computer Science, 19(8), 998-1014. https://doi.org/10.3844/jcssp.2023.998.1014

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Keywords

  • Alzheimer's Disease
  • 3D MRI Brain Neuroimaging
  • Biomarker Feature
  • Feature Extraction
  • Deep Generative Adversarial Networks
  • Fuzzy Neutrosophic Logic Region Growing
  • Whale Optimize
  • Image Enhancement