Akademik Veri Yönetim Sistemi
3rd International Congress on Engineering Sciences and Multidisciplinary Approaches, İstanbul, Türkiye, 10 Şubat 2022, ss.552-558
Abstract: Autism spectrum disorders (ASD) are among the diseases of our age that cannot be treated, have
a long diagnosis period, and have a high rate of disadvantage. Its incidence is estimated to be increasing
gradually. Definitive and rapid diagnosis of autism in early childhood can reduce the negative effects of this
disease. In this study, a deep learning model was applied for automatic diagnosis of autism with facial images. The deep convolutional neural network (DCNN) model, which is one of the most popular deep learning
methods used in many fields in recent years among many deep learning methods, was designed by utilizing
the pre-trained Resnet-18 model. Through the transfer learning approach, the designed deep learning model
was trained with the new dataset without changing its weights and was also tested with a randomly selected
20% of the whole dataset. As a result of the test process, ASD diagnosis was made with an accuracy rate of
82.3%. The success of the study has been proven both by the test result and by comparing it with other ASD
diagnostic studies and methods.
Keywords: ASD, Deep Learning, Transfer Learning, DCNN, Resnet-18