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Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
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Energy Efficient Data Mining Approach for Estimating the Diabetes
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Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure
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Prediction of Energy Consumption by Ships at the port using Deep Learning
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A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions
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Unmanned Aerial Vehicle with Thermal Imaging for Automating Water Status in Vineyard
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Home / Archives / Volume-3 / Issue-3 / Article-5

Volume - 3 | Issue - 3 | september 2021

Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
Pages: 214-229
Published
16 November, 2021
Abstract

Alzheimer's Disorder (AD) may permanently impair memory cells, resulting in dementia. Researchers say that early Alzheimer's disease diagnosis is difficult. MRI is used to detect AD in clinical trials. It requires high discriminative MRI characteristics to accurately classify dementia stages. Due to the large extraction of features, improved deep CNN-based models have recently proven accurate. With fewer picture samples in the datasets, over-fitting issues arise, limiting the effectiveness of deep learning algorithms. This research article minimizes the overfitting error due to fusion techniques. This hybrid approach is used to classify Alzheimer's disease more accurately than other traditional approaches. Besides, the Convolutional Neural Network (CNN) provides more minute features of small changes in MRI scan images than any other algorithm. Therefore, the proposed algorithm provides great accuracy in the region of sagittal, coronal, and axial Mild Cognitive Impairments (MCI) in the brain segment classification. Moreover, this research article compares the proposed algorithm with previous research output that is used to help prove its superiority. The performance metrics uses Health Subject (HS), MCI, and Mini-Mental State Evaluation (MMSE) to evaluate the proposed research algorithm.

Keywords

Alzheimer disease convolutional neural network multimodal fusion deep learning batch normalization group normalization human brain classification

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