Alzheimer’s disease detection using deep neural network in densenet 169

DOI

10.2991/978-94-6463-250-7_5How to use a DOI?

Keywords

Convolutional Neural network; Neuroimaging.dementia; DenseNet169

Abstract

Globally, Dementia is most frequently caused by Alzheimer’s disease (AD). From range in severity, it steadily gets worse, making it challenging for the individual to complete any work without assistance. Due to populations growth and the frequency of diagnoses, it starts to surpass. Existing methods for identifying cases include taking into account medical history, conducting cognitive challenging, then using magnetic resonance imaging (MRI); However, since they lack sensitivity and accuracy, successful approaches are uneven. A system for identifying certain MRI characteristics linked to Alzheimer’s disease is developed using the convolutional neural network (CNN). By taking into consideration the four phases of dementia and making a diagnose, the proposed system creates high-resolution diseases probabilities mapped from the local structural brain to a multilayer perceptron and gives accurate, clear visualization of particular Alzheimer’s disease risk. To avoid class imbalance, the sampling should be evenly distributed among the four main MRI image types. Mildly demented, moderately demented, non-demented, and very mildly demented are the grades assigned by the DenseNet169 algorithm. There is a serious class imbalance issue with the MRI image dataset that was collected through Kaggle. To identify the phases of dementia using MRI, a DenseNet169 algorithm classification is suggested. We also used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets to estimate AD categories, which is preferable than existing methods, in order to assess the proficiency of the proposed approach.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Ashwini
AU  - K. Valarmathi
AU  - B. Vanipriya
AU  - M. Devi
PY  - 2023
DA  - 2023/10/17
TI  - Alzheimer’s disease detection using deep neural network in densenet 169
BT  - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
PB  - Atlantis Press
SP  - 20
EP  - 25
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-250-7_5
DO  - 10.2991/978-94-6463-250-7_5
ID  - Ashwini2023
ER  -