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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 11, 2025.
Abstract: Psychological and mental health issues affect many people worldwide. However, the impact of these issues is stronger on children starting from early ages until their teenage. Using drawing to analyze and detect such feelings is a common way for specialists to help children express their feelings. Due to that, this study employs artificial intelligence to improve and ease this process by developing an application to help parents and specialists have a first-look analysis of their children's drawings. The Anamel application allows users to go through an experience that simulates clinics and psychological centers by being able to detect several feelings: happiness, sadness, anger, and aggression. Users start by answering pre-questions to collect initial information about the child. Then they can upload the image of the drawing, which is processed using computer vision techniques, where the AI model based on YOLO deep learning architecture provides the analysis results with an accuracy of 94%. Finally, they answer the post questions to ensure the final result. Specialists can also register themselves in the application to allow parents to communicate with them for extra help.
Amal Alshahrani, Manar Mohammed Almatrafi, Jenan Ibrahim Mustafa, Layan Saad Albaqami and Raneem Abdulrahman Aljabri. “Anamel: Children’s Psychological and Mental Health Detection Application by Drawing Analysis Based on AI”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161107
@article{Alshahrani2025,
title = {Anamel: Children’s Psychological and Mental Health Detection Application by Drawing Analysis Based on AI},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161107},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161107},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Amal Alshahrani and Manar Mohammed Almatrafi and Jenan Ibrahim Mustafa and Layan Saad Albaqami and Raneem Abdulrahman Aljabri}
}
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