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CNN-MobilenetV2- deep learning-based Alzheimer's disease prediction and classification

Alzheimers disease (AD) is a long-lasting brain disorder for which there is no effective treatment. Yet early detection can delay he growth of the disease. Due to the varied nature of medical tests, manual comparison, visualization and analysis of data can be time-consuming as well as demanding. As...

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Main Authors: A.V, Ambili, Senthil Kumar, A.V., Latip, Rohaya
Format: Article
Language:English
Published: Little Lion Scientific 2023
Online Access:http://psasir.upm.edu.my/id/eprint/107045/1/CNN-MobilenetV2-%20deep%20learning-based%20Alzheimers%20disease%20prediction%20and%20classification.pdf
http://psasir.upm.edu.my/id/eprint/107045/
https://www.jatit.org/volumes/Vol101No9/32Vol101No9.pdf
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spelling my.upm.eprints.1070452024-10-17T03:54:08Z http://psasir.upm.edu.my/id/eprint/107045/ CNN-MobilenetV2- deep learning-based Alzheimer's disease prediction and classification A.V, Ambili Senthil Kumar, A.V. Latip, Rohaya Alzheimers disease (AD) is a long-lasting brain disorder for which there is no effective treatment. Yet early detection can delay he growth of the disease. Due to the varied nature of medical tests, manual comparison, visualization and analysis of data can be time-consuming as well as demanding. As a result, an effective method for categorization of Magnetic Resonance Imaging (MRI) images is helpful but extremely difficult. In this paper, the stages of AD are identified using a unique method that effectively classifies brain MRI images using label propagation by involving a Deep Learning (DL)-based framework. Decreased brain tissue volume in brain lobes, hippocampus area, and thalamus are the primary features that aid in differentiating an AD from a normal MRI. The features should be efficient in distinguishing the characteristics between an AD-affected brain and a normal one. A Particle swarm optimization (PSO) based Speed-Up Robust Features (SURF) framework that embeds feature vectors in a subspace to maximize utilization of features that were extracted is presented. A classification method is employed in the newly generated space to categorize data into three classes namely, Normal Condition (NC), MCI, and AD using Convolution Neural Network (CNN)-MobileNetV2. The proposed scheme offers a classification accuracy is 97 yielding a 3 reduced error rate when compared to the best available approaches. Little Lion Scientific 2023-05 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/107045/1/CNN-MobilenetV2-%20deep%20learning-based%20Alzheimers%20disease%20prediction%20and%20classification.pdf A.V, Ambili and Senthil Kumar, A.V. and Latip, Rohaya (2023) CNN-MobilenetV2- deep learning-based Alzheimer's disease prediction and classification. Journal of Theoretical and Applied Information Technology, 101 (9). 3590 - 3600. ISSN 1992-8645; ESSN: 1817-3195 https://www.jatit.org/volumes/Vol101No9/32Vol101No9.pdf
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Alzheimers disease (AD) is a long-lasting brain disorder for which there is no effective treatment. Yet early detection can delay he growth of the disease. Due to the varied nature of medical tests, manual comparison, visualization and analysis of data can be time-consuming as well as demanding. As a result, an effective method for categorization of Magnetic Resonance Imaging (MRI) images is helpful but extremely difficult. In this paper, the stages of AD are identified using a unique method that effectively classifies brain MRI images using label propagation by involving a Deep Learning (DL)-based framework. Decreased brain tissue volume in brain lobes, hippocampus area, and thalamus are the primary features that aid in differentiating an AD from a normal MRI. The features should be efficient in distinguishing the characteristics between an AD-affected brain and a normal one. A Particle swarm optimization (PSO) based Speed-Up Robust Features (SURF) framework that embeds feature vectors in a subspace to maximize utilization of features that were extracted is presented. A classification method is employed in the newly generated space to categorize data into three classes namely, Normal Condition (NC), MCI, and AD using Convolution Neural Network (CNN)-MobileNetV2. The proposed scheme offers a classification accuracy is 97 yielding a 3 reduced error rate when compared to the best available approaches.
format Article
author A.V, Ambili
Senthil Kumar, A.V.
Latip, Rohaya
spellingShingle A.V, Ambili
Senthil Kumar, A.V.
Latip, Rohaya
CNN-MobilenetV2- deep learning-based Alzheimer's disease prediction and classification
author_facet A.V, Ambili
Senthil Kumar, A.V.
Latip, Rohaya
author_sort A.V, Ambili
title CNN-MobilenetV2- deep learning-based Alzheimer's disease prediction and classification
title_short CNN-MobilenetV2- deep learning-based Alzheimer's disease prediction and classification
title_full CNN-MobilenetV2- deep learning-based Alzheimer's disease prediction and classification
title_fullStr CNN-MobilenetV2- deep learning-based Alzheimer's disease prediction and classification
title_full_unstemmed CNN-MobilenetV2- deep learning-based Alzheimer's disease prediction and classification
title_sort cnn-mobilenetv2- deep learning-based alzheimer's disease prediction and classification
publisher Little Lion Scientific
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/107045/1/CNN-MobilenetV2-%20deep%20learning-based%20Alzheimers%20disease%20prediction%20and%20classification.pdf
http://psasir.upm.edu.my/id/eprint/107045/
https://www.jatit.org/volumes/Vol101No9/32Vol101No9.pdf
_version_ 1814054620165570560
score 13.211869

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