Predicting adolescent violence in Wartegg-ZeichenTest drawing images based on deep learning

Figures & data

Figure 1. Wartegg-ZeichenTest.

Table 1. Characteristics and meaning of the subject of each stimulus picture.

Stimulus picture No	Characteristics	Meaning
1st stimulus picture	Centre point	Centre, self-experience
2nd stimulus picture	Soft curve at the top left of the space	Stimulation of emotions and imagination
3rd stimulus picture	Three bars that become higher	Elevating rise, effort
4th stimulus picture	Rectangle	Heaviness, mental burden, trouble
5th stimulus picture	Intersecting slanted lines in the lower left corner	Suggestion of overcoming tension, hostility, inherent aggression
6th stimulus picture	Horizontal and vertical lines	Integration, completeness, unconsciousness
7th stimulus picture	Semicircle drawn with dots	Delicacy, sensitive sensitivity, empathy
8th stimulus picture	Arched curve, bow-shaped	Harmony, relief, a sense of security

Figure 2. Convolutional neural network (CNN) technique.

Figure 3. Convolutional neural network with the support vector machine (CNN(SVM)) technique.

Figure 4. Style transfer GAN Technique

Figure 5. A collection of WZT 5 stimulation pictures for violent students

Figure 6. Test dataset of WZT 5 stimulus drawing images of students who showed violence

Figure 7. Test dataset of WZT 5 stimulus drawing images of students who showed non-violence

Table 2. Confusion matrix results.

	TP	FN	FP	TN
CNN (softmax)	23	4	0	27
CNN (SVM)	26	1	0	27
CNN (SVM) + GAN	24	3	0	27
Ensemble	26	1	0	27

Notes: TP: true positive, FN: false negative, FP: false positive, TN: true negative, CNN: convolutional neural network, SVM: support vector machine, GAN: generative adversarial network

Table 3. Algorithm evaluation criterion value.

	Accuracy	Precision	Recall	F1
CNN (softmax)	0.92592	1	0.85185	0.92
CNN (SVM)	0.98148	1	0.96296	0.98
CNN (SVM) + GAN	0.94444	1	0.88888	0.94
Ensemble	0.98148	1	0.96296	0.98

Notes: CNN: convolutional neural network, SVM: support vector machine, GAN: generative adversarial network