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Cited by 42Dementia and Cognitive Impairment ResearchArtificial Intelligence in HealthcareBrain Tumor Detection and Classification

An Approach to Binary Classification of Alzheimer’s Disease Using LSTM

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Salehi, Waleed, Baglat, Preety, Gupta, Gaurav, Khan, Surbhi Bhatia, Almusharraf, Ahlam, Alqahtani, Ali, Kumar, Adarsh·2023·Bioengineering
DOI 10.3390/bioengineering10080950PMC PMC10451729
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Abstract

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer’s disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.

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Authors
Salehi, W., Baglat, P., Gupta, G., Khan, S. B., Almusharraf, A., Alqahtani, A., & Kumar, A.
Affiliations
  • Yogananda School of AI, Shoolini University, Bajhol 173229, India
  • Universidade da Madeira (PT)
  • Shoolini University (IN)
  • Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK
  • Princess Nourah bint Abdulrahman University (SA)
  • Najran University (SA)
  • Université Mohammed VI Polytechnique (MA)
Corresponding author
Kumar, Adarsh
ORCID
Venue
Bioengineering
Publisher
MDPI AG
Published
2023-08-09
Volume
10
Issue
8
Pages
950
ISSN
2306-5354 (ISSN-L)
DOI
10.3390/bioengineering10080950
PMC
PMC10451729
OpenAlex
W4385708368
Cited by
42
References
3
Citations by year
2026: 12 · 2025: 17 · 2024: 11 · 2023: 2
FWCI
8.55
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioengineering (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia (PNURSP2023R432)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
Keywords
Alzheimer's disease, binary classification, LSTM, magnetic resonance imaging, neuroimaging, disease prediction, web application, temporal pattern recognition

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