RESEARCH ARTICLE
Detection and Classification of the Different Stages of Alzheimer’s Disease using Sequential Convolutional Neural Network
Prakasam P.1, Aakash Gnanavel1, Karnan Rajendran2, *, Sailesh Suresh1
Article Information
Identifiers and Pagination:
Year: 2023Volume: 17
E-location ID: e187412072212260
Publisher ID: e187412072212260
DOI: 10.2174/18741207-v16-e221227-2022-HT27-3589-1
Article History:
Received Date: 10/3/2022Revision Received Date: 1/7/2022
Acceptance Date: 11/8/2022
Electronic publication date: 03/02/2023
Collection year: 2023
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Alzheimer's disease (AD) is a degenerative disorder known as mild or significant neurocognitive impairment diagnosed in older people and significantly reduces their quality of life. Memory issues are regularly one of the principal indications of Alzheimer's disease; however, beginning manifestations may vary from one individual to another. Detecting Alzheimer's disease is a time-consuming and challenging task but requires brain imaging reports and human expertise.
Objective:
More accurate and early diagnosis is essential for timely treatment and risk reduction. The accurate diagnosis of Alzheimer's disease plays an essential role in inpatient treatment, especially at the disease's early stages, because risk awareness allows the patients to undergo preventive measures even before the occurrence of irreversible brain damage. The main objective of this research is to diagnose Alzheimer's disease in its early stage through Magnetic Resonance Imaging (MRI).
Methodology:
To detect and classify the various stages of Alzheimer’s disease from the MRI, a Sequential Convolutional Neural Network (S-CNN) model is proposed, which incorporates the advantage of convolution operation and recurrent operation.
Results:
The combination of Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and the Open Access Series of Imaging Studies (OASIS) dataset obtained from the Kaggle is used for training and testing the proposed S-CNN model.
Conclusion:
The baseline DenseNet169 model has been fine-tuned and added sequentially to achieve better validation accuracy. From the validation, it is observed that it achieves an accuracy of 88.60% and outperforms other reported methods.
