#5789. A novel solution of an elastic net regularisation for dementia knowledge discovery using deep learning

July 2026publication date
Proposal available till 12-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Theoretical Computer Science;
Artificial Intelligence;
Software;
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Abstract:
Accurate classification of Magnetic Resonance Images (MRI) is essential to accurately predict Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) conversion. Meanwhile, deep learning has been successfully implemented to classify and predict dementia disease. However, the accuracy of MRI image classification is low. This paper aims to increase the accuracy and reduce the processing time of classification through Deep Learning Architecture by using Elastic Net Regularisation in Feature Selection. The proposed system consists of Convolutional Neural Network (CNN) to enhance the accuracy of classification and prediction by using Elastic Net Regularisation. Initially, the MRI images are fed into CNN for features extraction through convolutional layers alternate with pooling layers, and then through a fully connected layer. After that, the features extracted are subjected to Principle Component Analysis (PCA) and Elastic Net Regularisation for feature selection. Finally, the selected features are used as an input to Extreme Machine Learning (EML) for the classification of MRI images.
Keywords:
classification; convolutional neural network; Deep learning; dementia prediction; elastic net regularisation; extreme machine learning

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