CNN-based Cognitive Impairment Prediction using Handwriting Recognition and Analysis

Dysgraphia is a part of Cognitive impairment and it hampers a child's writing skills. Child used to write misconstrued and teacher used to feel that child is having laziness or a lack of enthusiasm in academics and it leading to more frustration and low self-esteem. Diagnosis and rehabilitation of dysgraphia are delayed by the subjective nature of traditional assessment approaches and their heavy reliance on professional knowledge. Furthermore, the assessment procedure's accessibility is sometimes compromised by the requirement for specialized equipment and personnel with the necessary skills. In other words, the current method of identifying cognitive impairment typically relies on subjective assessments provided by teachers or psychologist. In the study Cognitive Impairment Prediction Using Handwriting Analysis, we employed the deep learning methods like Convolution Neural Network algorithms and CNN-3. The CNN-2 and VGG16 are the next best models, but performance of VGG19 is poor. Performance analysis is a critical step in evaluating the effectiveness of a system or model. In deep learning, it is important to comprehend a model's performance on a certain task.