基于深度学习与多模态医学影像融合识别阈下抑郁患者

Identification of subthreshold depression based on deep learning and multimodal medical image fusion

目的基于深度学习(DL)卷积神经网络(CNN)算法,利用医学影像数据实现识别阈下抑郁(StD)患者。方法对56例StD患者和70名正常人采集MRI和fMRI数据,分别输入所构建的CNN,利用网络融合技术对2种不同模态数据进行综合分析,得到分类结果;最后调整网络结构与模型参数,实现分类效果最优化。结果单独MRI数据模型分类精度为73.02%,单独fMRI数据模型分类精度为65.08%;2种模态结合,最终分类精度升至78.57%。结论利用DL可识别StD患者与正常人;采用多种模态输入法可提高分类准确度。

Objective To explore the value of convolutional neural network(CNN)algorithm based on deep learning(DL)for identification of subliminal depression(StD)patients using medical image data.Methods MRI and fMRI data of 56 StD patients(StD group)and 70 normal controls(NC group)were collected and input into the constructed CNN,respectively.Then the network fusion technology was used to comprehensively analyze the two different modalities to obtain the classification result.Finally,the network fusion technology was used to integrate two different modal data and optimize the classification effect.Results The identification accuracy of the structural image data alone was 73.02%,of the functional image data alone was 65.08%.With combination of the two modes,the final classification accuracy raised to 78.57%.Conclusion DL can classify patients with StD and normal subjects.Multiple modal input methods can improve classification accuracy.