Dysgraphia disorder forecasting and classification technique using intelligent deep learning approaches

Writing abilities are impacted by dysgraphia, a condition of learning disability. It might be challenging to diagnose dysgraphia at an initial point of a child's upbringing. Problematic abilities linked to Dysgraphia difficulties that is utilized in detecting the learning disorder. The features used in this research to identify dysgraphia include handwriting and geometric features that is reclaimed using kekre-discrete cosine mathematical model. The feature learning step of deep transfer learning makes good use of the obtained features to identify dysgraphia. The results of the data collection indicate that this study can use handwritten images to detect children who have dysgraphia. Compared to past investigations, this experiment has shown a significant improvement in the capacity to identify dysgraphia using handwritten drawings. The proposed approach is compared with the machine learning and deep learning approaches where the Kekre-Discrete Cosine Transform with Deep Transfer Learning (K-DCT-DTL) outperforms the existing approaches. The proposed K-DCT-DTL approach attains 99.75% of highest accuracy that exhibits the efficiency of the proposed method.