Alzheimer’s Disease Classification Based on Image Transformation and Features Fusion

Abstract

It has become an inevitable trend for medical personnel to analyze and diagnose Alzheimer’s disease (AD) in different stages by combining functional magnetic resonance imaging (fMRI) and artificial intelligence technologies such as deep learning in the future. In this paper, a classification method was proposed for AD based on two different transformation images of fMRI and improved the 3DPCANet model and canonical correlation analysis (CCA). The main ideas include that, firstly, fMRI images were preprocessed, and subsequently, mean regional homogeneity (mReHo) and mean amplitude of low-frequency amplitude (mALFF) transformation were performed for the preprocessed images. Then, mReHo and mALFF images were extracted features using the improved 3DPCANet, and these two kinds of the extracted features were fused by CCA. Finally, the support vector machine (SVM) was used to classify AD patients with different stages. Experimental results showed that the proposed approach was robust and effective. Classification accuracy for significant memory concern (SMC) vs. mild cognitive impairment (MCI), normal control (NC) vs. AD, and NC vs. SMC, respectively, reached 95.00%, 92.00%, and 91.30%, which adequately proved the feasibility and effectiveness of the proposed method.

Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

“… is the average value at all times. Mean ReHo (mReHo) is obtained by dividing the average ReHo value of the entire brain (Cao et al, 2020 ; Jia et al, 2021 ; Pang et al, 2021 ).…”

“…ALFF is obtained by calculating the square root of the power spectrum and taking the average value over a predefined frequency range (Zang et al, 2007 ). Mean ALFF (mALFF) is calculated by dividing the mean ALFF value of the entire brain (Cao et al, 2020 ; Jia et al, 2021 ; Pang et al, 2021 ). In addition, several researchers have broadened the range of brain disease classification characteristics from different perspectives.…”

“…Chen et al ( 2023 ) constructed graph measures and FC from rs-fMRI data based on the AAL atlas to distinguish the Idiopathic PD (IPD) and the parkinsonian variant of multiple system atrophy (MSA-P). Jia et al ( 2021 ) extracted mReHo and mALFF from the Brainnetome atlas to distinguish MCI and AD, achieving an accuracy of 86.4%. Ghafoori and Shalbaf ( 2022 ) utilized clinical features and functional images extracted from rs-fMRI data to distinguish stable MCI and progressive MCI, with an accuracy improvement of 1.9% compared to using only functional images.…”