Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features

Abstract

Alzheimer's disease (AD) is a brain disorder that causes memory loss and behavioral and thinking problems. The symptoms of Alzheimer's are similar throughout its development stages, which makes it difficult to diagnose manually. Therefore, artificial intelligence (AI) techniques address the limitations of manual diagnosis. In this study, the images were enhanced and the active contour algorithm (ACA) was used to extract regions of interest (ROI) such as soft tissue and white matter. Strategies have been developed to diagnose AD and differentiate its stages. The first strategy is using XGBoost and ANN networks with the features of MobileNet, DenseNet, and GoogLeNet models. The second strategy is by XGBoost and ANN networks with combined features of MobileNet-DenseNet121, DenseNet121-GoogLeNet and MobileNet-GoogLeNet. The third strategy combines XGBoost and ANN networks with combined features of MobileNet-DenseNet121-Handcrafted, DenseNet121-GoogLeNet-Handcrafted, and MobileNet-GoogLeNet-Handcrafted leading to improved accuracy of the strategies and improved efficiency. XGBoost with hybrid features of DenseNet-GoogLeNet-Handcrafted achieved an AUC of 98.82%, accuracy of 98.8%, sensitivity of 98.9%, accuracy of 97.08%, and specificity of 99.5%.

Keywords: ACA; AD; ANN; CNN; Fusion features; RFE; XGBoost.

Fig. 1

Flowchart of the proposed systems for classifying Alzheimer’s disease.

Fig. 2

presents a selection of images extracted from the ADNI dataset pertaining to AD. Subfigure (a) displays the original images (b) showcases the same images following an enhancement process.

Fig. 3

Samples from all classes of the ADNI dataset for AD after segmentation by the ACA method (a) Original images (b) Segmentation (c, d) Region of interest.

Fig. 4

Strategies of fMRI image analysis for the ADNI data set for AD by CNN-ANN and CNN-XGBoost.

Fig. 5

Strategies of fMRI image analysis for ADNI dataset for AD by XGBoost and ANN with integrated CNN features.

Fig. 6

Strategies of fMRI image analysis for ADNI dataset for AD by XGBoost and ANN with integrated CNN features and handcrafted features.

Fig. 7

Production of confusion matrix from the XGBoost network with CNN features for diagnosing the ADNI data set for AD.

Fig. 8

Production of confusion matrix from the ANN network with CNN features for diagnosing the ADNI data set for AD.

Fig. 9

Production of confusion matrix from the XGBoost network with combined CNN features for diagnosing the ADNI data set of AD.

Fig. 10

Production of confusion matrix from the ANN network with combined CNN features for diagnosing the ADNI data set of AD.

Fig. 11

Production of confusion matrix from the XGBoost network with combined CNN features along with handcrafted features for diagnosing the ADNI data set of AD.

Fig. 12

Production of confusion matrix from the ANN network with combined CNN features along with handcrafted features for diagnosing the ADNI data set of AD.