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In this paper, we address the problem of children drawing classification by enhancing MobileNet through Salp Swarm Optimization (SSO). The proposed solution increases the classification accuracy with less computational processing time. The experimental setup uses the Kids’ Hand Movement Dataset consisting of 1000 pictures from fifty children with normal and fifty children with not-normal hand drawings streams. Especially, GridSearchCV-based hyperparameter is applied when the MobileNet model is tuned to avoid overfitting and reach the best performance. The experiments showed that the proposed method provided a notable performance gain in terms of accuracy in comparison with baseline model.

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Enhancing Children’s Drawing Classification with Salp Swarm Optimization-Optimized MobileNet: A Deep Learning Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Ali Ibrahim Khlaif,
  • Mohamed Saber Naceur,
  • Monji Kherallah

摘要

In this paper, we address the problem of children drawing classification by enhancing MobileNet through Salp Swarm Optimization (SSO). The proposed solution increases the classification accuracy with less computational processing time. The experimental setup uses the Kids’ Hand Movement Dataset consisting of 1000 pictures from fifty children with normal and fifty children with not-normal hand drawings streams. Especially, GridSearchCV-based hyperparameter is applied when the MobileNet model is tuned to avoid overfitting and reach the best performance. The experiments showed that the proposed method provided a notable performance gain in terms of accuracy in comparison with baseline model.