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. 2022 Jul 6:2022:4795307.
doi: 10.1155/2022/4795307. eCollection 2022.

Classification Algorithms for Brain Magnetic Resonance Imaging Images of Patients with End-Stage Renal Disease and Depression

Yan Cheng  1 , Tengwei Liao  2 , Nailong Jia  3
Affiliations

Classification Algorithms for Brain Magnetic Resonance Imaging Images of Patients with End-Stage Renal Disease and Depression

Yan Cheng et al. Contrast Media Mol Imaging. .

Abstract

This study was aimed to explore the relationship between depression and brain function in patients with end-stage renal disease (ESRD) complicated with depression based on brain magnetic resonance imaging (MRI) image classification algorithms. 30 people in the healthy control group and 70 people in the observation group were selected as the research objects. First, the preprocessing algorithms were applied on MRI images. With the depression classification algorithm based on deep learning, the features were extracted from the capsule network to construct a classification network, and the network structure was compared to obtain the difference in the distribution of brain lesions. Different classifiers and degree centrality, functional connection, low-frequency amplitude ratio, and low-frequency amplitude were selected to analyze the effectiveness of features. In the deep learning method, the neural network model was constructed, and feature extraction and classification network were carried out. The classification layer was based on the capsule network. The results showed that the correct rate of the deep learning feature extraction network structure combined with the capsule network classification was 82.47%, the recall rate was 83.69%, and the accuracy was 88.79%, showing that the capsule network can improve the heterogeneity of depression. The combination of fractional amplitude of low-frequency fluctuation (fALFF), DC, and amplitude of low-frequency fluctuation (ALFF) can achieve the accuracy of 100%. In summary, MRI images showed that patients with depression may have neurological abnormalities in the white matter area. In this study, the classification algorithm based on brain MRI images can effectively improve the classification performance.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Schematic diagram of the standardization process of resting MRI images.
Figure 2
Figure 2
Feature extraction in preprocessing.
Figure 3
Figure 3
Classification algorithm of CapsNet.
Figure 4
Figure 4
Network classification results.
Figure 5
Figure 5
Integrated classification results. ∗compared with the other two groups, ∗P < 0.05.
Figure 6
Figure 6
MRI images of healthy people.
Figure 7
Figure 7
MRI images of a patient.
Figure 8
Figure 8
MRI image registration example.
See this image and copyright information in PMC

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