A Patch Based 3D CNN Approach for Diagnosing Early Stages of Alzheimer's Disease by Applying OBL-WOA Algorithm

Alzheimer's disease (AD) is one of the most degraded neurodegenerative brain disorders, with no treatments involved. Understanding the early stages of Alzheimer's disease would be necessary for treating the disease and preventing further Tumour Necrosis Factor α (TNF-α) degeneration cells....

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Vydáno v:Proceedings of ... IEEE International Conference on Cloud Computing and Big Data Analysis (Online) s. 455 - 459
Hlavní autoři: Kumari, Rashmi, Goel, Shivani, Das, Subhranil
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 26.04.2023
Témata:
Alzheimer Disease
Classification algorithms
Convolutional Neural Network
Convolutional neural networks
Deep learning
Magnetic resonance imaging
Medical services
Three-dimensional displays
Whale Optimization
Whale optimization algorithms
ISSN:2832-3734
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Abstract Alzheimer's disease (AD) is one of the most degraded neurodegenerative brain disorders, with no treatments involved. Understanding the early stages of Alzheimer's disease would be necessary for treating the disease and preventing further Tumour Necrosis Factor α (TNF-α) degeneration cells. Previous studies demonstrated the application of deep learning techniques for distinguishing AD from Normal Control (NC) by applying T1-weighted MRI images. This paper proposes a novel Patch Based Convolutional Neural Network (PB-CNN) network for classifying three binary classifications. A new optimization technique, the Opposition Based Learning- Whale Optimization Algorithm (OBL-WOA), has been proposed to update the weights of the proposed PB-CNN Network. 326 ADNI subjects are investigated for the feasibility of the proposed optimization technique, where the highest classification accuracy is achieved when compared with other state-of-the-art techniques. Moreover, the proposed technique could assist doctors in diagnosing the early stages of AD.
AbstractList Alzheimer's disease (AD) is one of the most degraded neurodegenerative brain disorders, with no treatments involved. Understanding the early stages of Alzheimer's disease would be necessary for treating the disease and preventing further Tumour Necrosis Factor α (TNF-α) degeneration cells. Previous studies demonstrated the application of deep learning techniques for distinguishing AD from Normal Control (NC) by applying T1-weighted MRI images. This paper proposes a novel Patch Based Convolutional Neural Network (PB-CNN) network for classifying three binary classifications. A new optimization technique, the Opposition Based Learning- Whale Optimization Algorithm (OBL-WOA), has been proposed to update the weights of the proposed PB-CNN Network. 326 ADNI subjects are investigated for the feasibility of the proposed optimization technique, where the highest classification accuracy is achieved when compared with other state-of-the-art techniques. Moreover, the proposed technique could assist doctors in diagnosing the early stages of AD.
Author Das, Subhranil
Kumari, Rashmi
Goel, Shivani
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  organization: BIT MESRA,Deapartment of EEE,Ranchi,Jharkhand,India
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Snippet Alzheimer's disease (AD) is one of the most degraded neurodegenerative brain disorders, with no treatments involved. Understanding the early stages of...
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StartPage 455
SubjectTerms Alzheimer Disease
Classification algorithms
Convolutional Neural Network
Convolutional neural networks
Deep learning
Magnetic resonance imaging
Medical services
Three-dimensional displays
Whale Optimization
Whale optimization algorithms
Title A Patch Based 3D CNN Approach for Diagnosing Early Stages of Alzheimer's Disease by Applying OBL-WOA Algorithm
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