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Image Classification in HTP Test Based on Convolutional Neural Network Model.

Lin Liu1

  • 1School of Art and Design, Wuhan University of Technology, Wuhan, Hubei 430070, China.

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The House-Tree-Person (HTP) test, a projective psychological assessment, can be objectively evaluated using deep learning's convolutional neural networks. This approach enhances reliability and validity in psychological evaluations, particularly for mental health diagnoses.

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Area of Science:

  • Psychometrics and Psychological Assessment
  • Computer Vision and Deep Learning

Background:

  • The House-Tree-Person (HTP) test is a widely used projective psychological assessment technique.
  • Traditional HTP assessment relies on subjective interpretation of drawing features by experienced researchers.
  • Deep learning, particularly convolutional neural networks (CNNs), excels at image recognition and feature learning.

Purpose of the Study:

  • To explore the application of convolutional neural networks (CNNs) for objective evaluation of the HTP test.
  • To address the subjectivity inherent in traditional HTP test interpretation.
  • To enhance the reliability and validity of HTP test scoring through automated feature extraction.

Main Methods:

  • Utilized convolutional neural networks (CNNs), a deep learning model, for image analysis of HTP drawings.
  • Leveraged CNNs' ability for automatic feature learning, bypassing manual feature extraction.
  • Focused on quantitative scoring and classification of HTP drawing characteristics.

Main Results:

  • CNNs can automatically learn and extract relevant features from HTP drawings.
  • This automated approach offers a more objective method for HTP test evaluation compared to traditional subjective scoring.
  • The method demonstrates potential for improved reliability and validity in psychological assessments.

Conclusions:

  • Convolutional neural networks offer a promising, objective approach to HTP test evaluation.
  • This deep learning application has high potential value in psychological evaluation and mental disease diagnosis.
  • Automated HTP analysis can standardize assessments and reduce reliance on individual researcher experience.