Affiliation:
1. Department of Automation Science Beihang University Beijing China
2. Department of IT Services University of Okara Okara Punjab Pakistan
3. MLC Lab Okara Punjab Pakistan
4. Department of Computing Sciences School of Technology and Innovations, University of Vaasa Vaasa Finland
5. Department of Computer Science University of Okara Okara Punjab Pakistan
Abstract
AbstractAutism spectrum disorder (ASD) is a complex psychological syndrome characterized by persistent difficulties in social interaction, restricted behaviours, speech, and nonverbal communication. The impacts of this disorder and the severity of symptoms vary from person to person. In most cases, symptoms of ASD appear at the age of 2 to 5 and continue throughout adolescence and into adulthood. While this disorder cannot be cured completely, studies have shown that early detection of this syndrome can assist in maintaining the behavioural and psychological development of children. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre‐trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. Transfer learning was applied to every model included in the study to achieve higher results than the initial models. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models. The proposed method also outperformed the state‐of‐the‐art models in terms of accuracy and computational cost.
Publisher
Institution of Engineering and Technology (IET)
Subject
Health Information Management,Health Informatics
Cited by
1 articles.
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