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A Deep Learning Frame-Work for Recognizing Developmental Disorders

Developmental Disorders are chronic disabilities that have a severe impact on the day to day functioning of a large section of the human population. Recognizing developmental disorders from facial images is an important but a relatively unexplored challenge in the field of computer vision. This paper proposes a novel framework to detect developmental disorders from facial images. A spectrum of disorders constituting of Autism Spectrum Disorder, Cerebral Palsy, Fetal Alcohol Syndrome, Down syndrome, Intellectual disability and Progeria have been considered for recognition. The framework relies on Deep Convolutional Neural Networks (DCNN) for feature extraction. A new data-set comprising of images of subjects with these disabilities was built for testing the performance of the frame work. This model has been tested on different age groups, individual disabilities and has also been compared to a similar model that uses human intelligence to identify different developmental disorders. The results indicate that the model performs better than average human intelligence in terms of differentiating amongst different disabilities and is able to recognize subjects with these developmental disorders with an accuracy of 98.80%.