This study used MRI brain image segmentation to identify novel magnetic resonance imaging (MRI) biomarkers to distinguish patients with schizophrenia (SCZ), major depressive disorder (MD), and healthy control (HC). Brain texture measurements, including entropy and contrast, were calculated to capture variability in adjacent MRI voxel intensity. These measures are then applied to group classification in deep learning techniques and combined with hierarchical correlations to locate results. Texture feature maps were extracted from segmented brain MRI scans of 141 patients with schizophrenia (SCZ), 103 patients with major depressive disorder (MD) and 238 healthy controls (HC). Gray scale coassociation matrix (GLCM) is a monomer matrix calculated in a voxel cube. Deep learning methods were evaluated to determine the application capability of texture feature mapping in binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method is implemented by repeated nesting and cross-validation for feature selection. Regions that show the highest correlation (positive or negative). In this study, the authors successfully classified SCZ, MD and HC. This suggests that texture analysis can be used as an effective feature extraction method to distinguish different disease states. Compared with other methods, texture analysis can capture richer image information and improve classification accuracy in some cases. The classification accuracy of SCZ and HC, MD and HC, SCZ and MD reached 84.6%, 86.4% and 76.21%, respectively. Among them, SCZ and HC are the most significant features with high sensitivity and specificity.

中文翻译：

---

基于神经网络级联异常纹理信息传播的精神分裂症和抑郁症患者分类

本研究使用 MRI 脑图像分割来识别新型磁共振成像 (MRI) 生物标志物，以区分精神分裂症 (SCZ)、重度抑郁症 (MD) 和健康对照 (HC) 患者。计算大脑纹理测量值，包括熵和对比度，以捕获相邻 MRI 体素强度的变化。然后将这些测量应用于深度学习技术中的组分类，并结合层次相关性来定位结果。从 141 名精神分裂症患者 (SCZ)、103 名重度抑郁症患者 (MD) 和 238 名健康对照者 (HC) 的分段脑 MRI 扫描中提取纹理特征图。灰度共关联矩阵 (GLCM) 是在体素立方体中计算的单体矩阵。评估深度学习方法以确定纹理特征映射在二元分类（SCZ 与 HC、MD 与 HC、SCZ 与 MD）中的应用能力。该方法通过重复嵌套和交叉验证来实现特征选择。显示最高相关性（正或负）的区域。在这项研究中，作者成功地对 SCZ、MD 和 HC 进行了分类。这表明纹理分析可以作为一种有效的特征提取方法来区分不同的疾病状态。与其他方法相比，纹理分析在某些情况下可以捕获更丰富的图像信息并提高分类精度。 SCZ与HC、MD与HC、SCZ与MD的分类准确率分别达到84.6%、86.4%和76.21%。其中SCZ和HC是最显着的特征，具有较高的敏感性和特异性。