Affiliations 

  • 1 Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia
  • 2 Information Technology Services, University of Okara, Okara, Pakistan
  • 3 Department of CS, University of Okara, Okara, Pakistan
  • 4 Department of Computer Science, Government College Women University, Sialkot, Pakistan
  • 5 Department of Biotechnology, College of Science, Taif University, Taif, Saudi Arabia
  • 6 Department of chemistry, College of Science, Taif University, Taif, Saudi Arabia
  • 7 Department of Animal Breeding and Genetics, Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
PLoS One, 2024;19(9):e0304995.
PMID: 39240975 DOI: 10.1371/journal.pone.0304995

Abstract

Alzheimer's disease (AD) is a brain illness that causes gradual memory loss. AD has no treatment and cannot be cured, so early detection is critical. Various AD diagnosis approaches are used in this regard, but Magnetic Resonance Imaging (MRI) provides the most helpful neuroimaging tool for detecting AD. In this paper, we employ a DenseNet-201 based transfer learning technique for diagnosing different Alzheimer's stages as Non-Demented (ND), Moderate Demented (MOD), Mild Demented (MD), Very Mild Demented (VMD), and Severe Demented (SD). The suggested method for a dataset of MRI scans for Alzheimer's disease is divided into five classes. Data augmentation methods were used to expand the size of the dataset and increase DenseNet-201's accuracy. It was found that the proposed strategy provides a very high classification accuracy. This practical and reliable model delivers a success rate of 98.24%. The findings of the experiments demonstrate that the suggested deep learning approach is more accurate and performs well compared to existing techniques and state-of-the-art methods.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.