A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer’s Disease using MRI Images

Abstract

Recently, the early diagnosis of Alzheimer’s disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble method based on deep learning for the early diagnosis of AD using MRI images. The methodology of this study consisted of collecting the dataset, preprocessing, creating the individual and ensemble models, evaluating the models based on ADNI data, and validating the trained model based on the local dataset. The proposed method was an ensemble approach selected through a comparative analysis of various ensemble scenarios. Finally, the six best individual CNN-based classifiers were selected to combine and constitute the ensemble model. The evaluation showed an accuracy rate of 98.57, 96.37, 94.22, 99.83, 93.88, and 93.92 for NC/AD, NC/EMCI, EMCI/LMCI, LMCI/AD, four-way and three-way classification groups, respectively. The validation results on the local dataset revealed an accuracy of 88.46 for three-way classification. Our performance results were higher than most reviewed studies and comparable with others. Although comparative analysis showed superior results of ensemble methods against individual architectures, there were no significant differences among various ensemble approaches. The validation results revealed the low performance of individual models in practice. In contrast, the ensemble method showed promising results. However, further studies on various and larger datasets are required to validate the generalizability of the model.

Intelligent prediction of Alzheimer’s disease via improved multifeature squeeze-and-excitation-dilated residual network

“…Once, Arafa et al 17 used a traditional CNN model for the classification of mild-dementia and non-dementia with an accuracy of 99.99%. And Fathi et al 18 applied six CNN classifiers to form an integrated model with up to 93.92% accuracy. Then, Helaly et al 19 used the traditional CNN model, the classification accuracies for 2D and 3D data of AD were 93.61 and 95.17%, respectively, and VGG19 models for transfer learning with up to 97% accuracy.…”

Enhancing Alzheimer’s disease diagnosis and staging: a multistage CNN framework using MRI

“…Recently, Fathi et al. ( 30 ) introduced a weighted probability-based ensemble method to combine six 2D-CNN architectures and obtain a high classification rate of 93.88 for four classes. Furthermore, they compared different ensemble methods and showed that the ensemble methods yielded better results than individual architectures.…”