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This is a Human Clinical Trial study (also classified as: Observational).

Dysgraphia Disorder Detection And Classification Using Enhanced Adaptive Butterfly Optimization Algorithm

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The method achieved 99.2% accuracy in detecting dysgraphia, highlighting its effectiveness.
Using handwritten images, the approach integrates features from handwriting and geometry to detect difficulty.
The method employs deep transfer learning, enabling efficient feature extraction for enhanced detection.
The results suggest that handwritten task data can significantly aid in diagnosing dysgraphia in children.

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Proposed algorithm achieves 99.2% accuracy in identifying dysgraphia through handwriting analysis.

Balamurugan et al. (2025) studied this question.

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