phat 24(1):

Research Article

Early Alzheimer’s Disease Detection Using Deep Learning

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  • @ARTICLE{10.4108/eetpht.9.3966,
        author={Kokkula Lokesh and Nagendra Panini Challa and Abbaraju Sai Satwik and Jinka Chandra Kiran and Narendra Kumar Rao and Beebi Naseeba},
        title={Early Alzheimer’s Disease Detection Using Deep Learning},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={9},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2023},
        month={9},
        keywords={Classification Detection, Deep Learning, AzNet, DenseNet, ResNet, EfficientNet, InceptionNet},
        doi={10.4108/eetpht.9.3966}
    }
    
  • Kokkula Lokesh
    Nagendra Panini Challa
    Abbaraju Sai Satwik
    Jinka Chandra Kiran
    Narendra Kumar Rao
    Beebi Naseeba
    Year: 2023
    Early Alzheimer’s Disease Detection Using Deep Learning
    PHAT
    EAI
    DOI: 10.4108/eetpht.9.3966
Kokkula Lokesh1, Nagendra Panini Challa1,*, Abbaraju Sai Satwik1, Jinka Chandra Kiran1, Narendra Kumar Rao2, Beebi Naseeba1
  • 1: Vellore Institute of Technology University
  • 2: Mohan Babu University
*Contact email: nagendra.challa@vitap.ac.in

Abstract

The early detection of Alzheimer's disease, a neurodegenerative ailment that affects both cognitive and social functioning, can be accomplished using deep learning technology. Deep learning is more accurate and efficient than human diagnosis in detecting functional connectivity and changes in the brain networks of people with MCI. Early detection of Mild Cognitive Impairment (MCI) can reduce the disease's development. However, achieving high accuracy levels is difficult due to the dearth of reliable biomarkers. The dataset was picked up from the Kaggle database. It contains magnetic resonance images of the brain, each image being unique and in different stages of the disease for classification purpose for our project, as it was most suitable for our project’s needs. We developed a deep learning model using learning AZ net, Dense net, Resnet, Efficient Net and Inception Net with a maximum accuracy of 99.96% for classifying Alzheimer's disease stages and early detection using transfer learning and other approaches.