IEEE Access (Jan 2023)
Classification of Alzheimer’s Disease Using Ensemble Convolutional Neural Network With LFA Algorithm
Abstract
Alzheimer’s disease (AD) is a disease that develops gradually, ultimately causing deterioration of brain functions. Thus, early diagnosis is essential for treating and managing AD. Magnetic-resonance-imaging (MRI)-based AD diagnosis classifies the stage of AD according to the extent of atrophy caused to a patient’s hippocampal and entorhinal cortex. In this case, the shape of the patient’s brain serves as a crucial feature. Therefore, in this paper, we propose an ensemble convolutional neural network (CNN) model that can classify the AD stage according to the shape of a patient’s brain. The proposed model is structured by combining a convolutional layer part of the visual geometry group network (VGGNet) model, with proven performance in image classification, and a 1D CNN model into a pipeline. Here, the 1D CNN applies the line segment feature analysis (LFA) algorithm to MRI images to transform the visual line segment information of the images into vectors and record strong features indicating the shape of the brain. This is followed by 1D CNN model training. Notably, the 1D CNN model can carefully observe the brain shape owing to the parallel connection of ten 1D convolutional layers with LFA features. Subsequently, the brain shape information is combined with features obtained from the original image through the VGGNet to improve the model performance compared to that of existing methods. To evaluate the performance of the proposed ensemble CNN model, MRI datasets collected from Kaggle are used to evaluate and compare the proposed model with existing image classification methods and methods proposed in related studies. The experimental results reveal that the proposed model demonstrates superior performance with an accuracy of 0.986 and a loss of 0.0385.
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