Non-segmented MRI brain images are used for the identification of new Magnetic Resonance Imaging (MRI) biomarkers able to differentiate between schizophrenic patients (SCZ), major depressive patients (MD) and healthy controls (HC). Brain texture measures such as entropy and contrast, capturing the neighboring variation of MRI voxel intensities, were computed and fed into deep learning technique for group classification. Layer-wise relevance was applied for the localization of the classification results. Texture feature map of non-segmented brain MRI scans were extracted from 141 SCZ, 103 MD and 238 HC. The gray level co-occurrence matrix (GLCM) was calculated on a voxel-by-voxel basis in a cube of voxels. Deep learning tested if texture feature map could predict diagnostic group membership of three classes under a binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method was applied in a repeated nested cross-validation scheme and cross-validated feature selection. The regions with the highest relevance (positive/negative) are presented. The method was applied on non-segmented images reducing the computation complexity and the error associated with segmentation process.

非分段 MRI 脑图像用于识别能够区分精神分裂症患者 (SCZ)、重度抑郁症患者 (MD) 和健康对照 (HC) 的新磁共振成像 (MRI) 生物标志物。计算熵和对比度等脑纹理测量值，捕获 MRI 体素强度的相邻变化，并将其输入深度学习技术进行组分类。分层相关性应用于分类结果的定位。从 141 个 SCZ、103 个 MD 和 238 个 HC 中提取非分段脑部 MRI 扫描的纹理特征图。灰度共生矩阵 (GLCM) 是在体素立方体中逐个体素计算的。深度学习测试了纹理特征图是否可以预测二进制分类下三个类别的诊断组成员资格（SCZ 与 HC，MD 与 HC、SCZ 与 MD）。该方法应用于重复嵌套的交叉验证方案和交叉验证的特征选择。呈现了具有最高相关性（正/负）的区域。该方法应用于非分割图像，降低了计算复杂度和与分割过程相关的误差。