Classification of Alzheimer’s Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network

Autor: Monika Sethi, Shalli Rani, Puneet Bawa, Sachin Ahuja, Atef Zaguia
Rok vydání: 2021
Předmět:
Boosting (machine learning)
Article Subject
Computer science
Computer applications to medicine. Medical informatics
Bayesian probability
Normal Distribution
R858-859.7
Neuroimaging
Machine learning
computer.software_genre
Multimodal Imaging
Convolutional neural network
General Biochemistry
Genetics and Molecular Biology
Deep Learning
Imaging
Three-Dimensional
Alzheimer Disease
Humans
Leverage (statistics)
Cognitive Dysfunction
Hyperparameter
General Immunology and Microbiology
business.industry
Applied Mathematics
Deep learning
Bayesian optimization
Computational Biology
Bayes Theorem
General Medicine
Prognosis
Magnetic Resonance Imaging
Bayesian search theory
Early Diagnosis
Case-Control Studies
Modeling and Simulation
Neural Networks
Computer
Artificial intelligence
business
computer
Research Article
Zdroj: Computational and Mathematical Methods in Medicine, Vol 2021 (2021)
Computational and Mathematical Methods in Medicine
ISSN: 1748-6718
1748-670X
DOI: 10.1155/2021/4186666
Popis: Alzheimer’s disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.
Databáze: OpenAIRE
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