Wang Huayi, Zhang Jie, Huang Yaocheng, Cai Bo
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China.
Entropy (Basel). 2023 Sep 17;25(9):1350. doi: 10.3390/e25091350.
The House-Tree-Person (HTP) sketch test is a psychological analysis technique designed to assess the mental health status of test subjects. Nowadays, there are mature methods for the recognition of depression using the HTP sketch test. However, existing works primarily rely on manual analysis of drawing features, which has the drawbacks of strong subjectivity and low automation. Only a small number of works automatically recognize depression using machine learning and deep learning methods, but their complex data preprocessing pipelines and multi-stage computational processes indicate a relatively low level of automation. To overcome the above issues, we present a novel deep learning-based one-stage approach for depression recognition in HTP sketches, which has a simple data preprocessing pipeline and calculation process with a high accuracy rate. In terms of data, we use a hand-drawn HTP sketch dataset, which contains drawings of normal people and patients with depression. In the model aspect, we design a novel network called Feature-Enhanced Bi-Level Attention Network (FBANet), which contains feature enhancement and bi-level attention modules. Due to the limited size of the collected data, transfer learning is employed, where the model is pre-trained on a large-scale sketch dataset and fine-tuned on the HTP sketch dataset. On the HTP sketch dataset, utilizing cross-validation, FBANet achieves a maximum accuracy of 99.07% on the validation dataset, with an average accuracy of 97.71%, outperforming traditional classification models and previous works. In summary, the proposed FBANet, after pre-training, demonstrates superior performance on the HTP sketch dataset and is expected to be a method for the auxiliary diagnosis of depression.
房树人（HTP）绘画测试是一种旨在评估测试对象心理健康状况的心理分析技术。如今，使用HTP绘画测试识别抑郁症已有成熟的方法。然而，现有工作主要依赖于对绘画特征的人工分析，存在主观性强和自动化程度低的缺点。只有少数工作使用机器学习和深度学习方法自动识别抑郁症，但它们复杂的数据预处理管道和多阶段计算过程表明自动化水平相对较低。为了克服上述问题，我们提出了一种基于深度学习的新颖单阶段方法，用于在HTP绘画中识别抑郁症，该方法具有简单的数据预处理管道和计算过程，且准确率高。在数据方面，我们使用一个手绘HTP绘画数据集，其中包含正常人和抑郁症患者的绘画。在模型方面，我们设计了一种名为特征增强双级注意力网络（FBANet）的新颖网络，它包含特征增强和双级注意力模块。由于收集到的数据量有限，采用了迁移学习，即在一个大规模绘画数据集上对模型进行预训练，并在HTP绘画数据集上进行微调。在HTP绘画数据集上，利用交叉验证，FBANet在验证数据集上的最大准确率达到99.07%，平均准确率为97.71%，优于传统分类模型和先前的工作。综上所述，所提出的FBANet在预训练后，在HTP绘画数据集上表现出卓越的性能，有望成为一种抑郁症辅助诊断方法。
