Alz-SAENet: A Deep Sparse Autoencoder based Model for Alzheimer’s Classification

Precise identification of Alzheimer's Disease (AD) is vital in health care, especially at an early stage, since recognizing the likelihood of incidence and progression allows patients to adopt preventive measures before irreparable brain damage occurs. Magnetic Resonance Imaging is an effective...

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Veröffentlicht in:International journal of advanced computer science & applications Jg. 13; H. 10
Hauptverfasser: Reddy, G Nagarjuna, Reddy, K Nagi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: West Yorkshire Science and Information (SAI) Organization Limited 2022
Schlagworte:
Alzheimer's disease
Artificial neural networks
Brain damage
Extraction procedures
Feature extraction
Magnetic resonance imaging
Neural networks
ISSN:2158-107X, 2156-5570
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Abstract Precise identification of Alzheimer's Disease (AD) is vital in health care, especially at an early stage, since recognizing the likelihood of incidence and progression allows patients to adopt preventive measures before irreparable brain damage occurs. Magnetic Resonance Imaging is an effective and common clinical strategy to diagnose AD due to its structural details. we built an advanced deep sparse autoencoder-based architecture, named Alz-SAENet for the identification of diseased from typical control subjects using MRI volumes. We focused on a novel optimal feature extraction procedure using the combination of a 3D Convolutional Neural Network (CNN) and deep sparse autoencoder (SAE). Optimal features derived from the bottleneck layer of the hyper-tuned SAE network are subsequently passed via a deep neural network (DNN). This approach results in the improved four-way categorization of AD-prone 3D MRI brain images that prove the capability of this network in AD prognosis to adopt preventive measures. This model is further evaluated using ADNI and Kaggle data and achieved 98.9% and 98.215% accuracy and showed a tremendous response in distinguishing the MRI volumes that are in a transitional phase of AD.
AbstractList Precise identification of Alzheimer's Disease (AD) is vital in health care, especially at an early stage, since recognizing the likelihood of incidence and progression allows patients to adopt preventive measures before irreparable brain damage occurs. Magnetic Resonance Imaging is an effective and common clinical strategy to diagnose AD due to its structural details. we built an advanced deep sparse autoencoder-based architecture, named Alz-SAENet for the identification of diseased from typical control subjects using MRI volumes. We focused on a novel optimal feature extraction procedure using the combination of a 3D Convolutional Neural Network (CNN) and deep sparse autoencoder (SAE). Optimal features derived from the bottleneck layer of the hyper-tuned SAE network are subsequently passed via a deep neural network (DNN). This approach results in the improved four-way categorization of AD-prone 3D MRI brain images that prove the capability of this network in AD prognosis to adopt preventive measures. This model is further evaluated using ADNI and Kaggle data and achieved 98.9% and 98.215% accuracy and showed a tremendous response in distinguishing the MRI volumes that are in a transitional phase of AD.
Author Reddy, G Nagarjuna
Reddy, K Nagi
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SubjectTerms Alzheimer's disease
Artificial neural networks
Brain damage
Extraction procedures
Feature extraction
Magnetic resonance imaging
Neural networks
Title Alz-SAENet: A Deep Sparse Autoencoder based Model for Alzheimer’s Classification
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