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Authors: Gopi Battineni ; Nalini Chintalapudi ; Francesco Amenta and Enea Traini

Affiliation: Telemedicine and Tele Pharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, Camerino, 62032, Italy

Keyword(s): Alzheimer’s Disease (AD), OASIS-3, MRI Images, Deep Learning, CNN.

Abstract: Alzheimer’s disease (AD) is one of the common medical issues that the world is facing today. This disease has a high prevalence of memory loss and cognitive decline primarily in the elderly. At present, there is no specific treatment for this disease, but it is thought that identification of it at an early stage can help to manage it in a better way. Several studies used machine learning (ML) approaches for AD diagnosis and classification. In this study, we considered the Open Access Series of Imaging Studies-3 (OASIS-3) dataset with 2,168 Magnetic Resonance Imaging (MRI) images of patients with very mild to different stages of cognitive decline. We applied deep learning-based convolution neural networks (CNN) which are well-known approaches for diagnosis-based studies. The model training was done by 70% of images and applied 10-fold cross-validation to validate the model. The developed architecture model has successfully classified the different stages of dementia images and achieve d 83.3% accuracy which is higher than other traditional classification techniques like support vectors and logistic regression. (More)

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Paper citation in several formats:
Battineni, G.; Chintalapudi, N.; Amenta, F. and Traini, E. (2021). Deep Learning Type Convolution Neural Network Architecture for Multiclass Classification of Alzheimer’s Disease. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - BIOIMAGING; ISBN 978-989-758-490-9; ISSN 2184-4305, SciTePress, pages 209-215. DOI: 10.5220/0010378600002865

@conference{bioimaging21,
author={Gopi Battineni. and Nalini Chintalapudi. and Francesco Amenta. and Enea Traini.},
title={Deep Learning Type Convolution Neural Network Architecture for Multiclass Classification of Alzheimer’s Disease},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - BIOIMAGING},
year={2021},
pages={209-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010378600002865},
isbn={978-989-758-490-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - BIOIMAGING
TI - Deep Learning Type Convolution Neural Network Architecture for Multiclass Classification of Alzheimer’s Disease
SN - 978-989-758-490-9
IS - 2184-4305
AU - Battineni, G.
AU - Chintalapudi, N.
AU - Amenta, F.
AU - Traini, E.
PY - 2021
SP - 209
EP - 215
DO - 10.5220/0010378600002865
PB - SciTePress

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