The Classification of Children with Autism using Deep Learning-Based Image Analysis This research presents the application of deep learning techniques to analyze children's drawings for efficient classification of autism spectrum disorder.
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Abstract
Currently, the diagnosis of autism in children still relies on behavioral observation and psychological tests, which may have limitations in terms of accuracy and speed. This research therefore applies machine learning techniques, especially convolutional neural networks (CNN) with the ResNet50 model, to analyze and classify drawings of children with and without autism. The samples were divided into groups of 5–8 and 9–12 years old, and the results were evaluated with Accuracy, Recall, Specificity, F1-Score, and Confusion Matrix. The experimental results showed that the model could classify drawings accurately, with an Accuracy of 81.9% and 89.5%, and an F1-Score of 0.83 and 0.91 for the 5–8 and 9–12 age groups, respectively. After using Data Augmentation, the accuracy increased to 87.7% and 91.1%, with an F1-Score of 0.89 and 0.93. However, the research still has limitations in terms of the size and variety of data, which should be expanded in the future to increase the accuracy of the model.
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