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. The result shows that the accuracy of the proposed solution is better than the current system. In addition to that, the proposed method has improved the classification accuracy by 5% on average and reduced the processing time by 30 ~ 40 seconds on average. The proposed system is focused on improving the accuracy and processing time of MCI converters/non-converters classification. It consists of features extraction, feature selection, and classification using CNN, FreeSurfer, PCA, Elastic Net, and Extreme Machine Learning. Finally, this study enhances the accuracy and the processing time by using Elastic Net Regularisation, which provides important selected features for classification.

摘要

磁共振图像 (MRI) 的准确分类对于准确预测轻度认知障碍 (MCI) 向阿尔茨海默病 (AD) 的转化至关重要。同时，深度学习已成功应用于痴呆症的分类和预测。然而，MRI图像分类的准确率较低。本文旨在通过在特征选择中使用弹性网络正则化，通过深度学习架构提高分类的准确性并减少处理时间。该系统由卷积神经网络（CNN）组成，通过使用弹性网络正则化来提高分类和预测的准确性。最初，MRI 图像被输入 CNN，通过卷积层和池化层交替进行特征提取，然后通过全连接层。在那之后，提取的特征经过主成分分析（PCA）和弹性网络正则化进行特征选择。最后，所选特征用作极限机器学习 (EML) 的输入，以对 MRI 图像进行分类。结果表明，所提出的解决方案的精度优于当前系统。除此之外，该方法的分类精度平均提高了5%，处理时间平均减少了30~40秒。所提出的系统专注于提高 MCI 转换器/非转换器分类的准确性和处理时间。它包括使用 CNN、FreeSurfer、PCA、Elastic Net 和 Extreme Machine Learning 进行特征提取、特征选择和分类。最后，