Exploring Convolutional Neural Networks Architectures for the Classification of Hand-Drawn Shapes in Learning Therapy Applications

Abstract

A positive consequence of the existence of a more inclusive society is the appearance of protocols to help identify those in need. One of these protocols is the application of tests with the aim to detect learning disabilities in children so that opportune intervention could be made. These advances, though, also pose challenges to the responsible to administer these tests, for example, the large number of tests to evaluate that make the process lengthy at the best. In this study, we implement a Convolutional Neural Network (CNN) model for the automatic classification of hand-drawn drawings of the Bender Gestalt Test (BGT), which is a test that evaluates the perceptual-motor maturity and perceptual disorder on individuals. In BGT, nine different drawings are presented to the patient who must reproduce them using pencil and paper. This study focuses on the automatic detection of the traces and their classification, then aiming to expedite the test evaluation process. Our proposed task-specialized CNN, named CNN4-Bender, is compared against other eleven neural-network based models registering an average performance of 91.56%, surpassed only by ResNet50 but with a high computational cost in this last one. To further evaluate our model we also consider other classification tasks that include the MNIST and OIHACDB datasets, where the CNN4-Bender architecture obtains a competitive performance and in some cases outperforms state-of-the-art models.