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In this study, we introduce a new technique based on image processing to predict “autism spectrum disorder” (ASD) images using a combination of transfer learning and VGG16 Convolutional Neural Network (CNN) architecture. Using an extensive dataset of 2540 training pics, 100 validation, and 300 test images, the model showed an accuracy of 71.33%, which is quite impressive. The proposed approach, by leveraging pre-trained weights on a large image dataset and fine-tuning ASD-specific data, holds great promise for effectively screening people with ASD. Conclusions The results indicate that deep learning methods could help to enhance the accuracy of ASD diagnosis and interventions, inherently improve early diagnosis and specialized therapy interventions for patients with ASD. In general, this research sets the stage for better and more individualized treatments for people with ASD. It also sets the ground for future research focusing on increasing the accuracy and robustness of machine learning models that could be used in ASD prediction and management.

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Assessing the Efficacy of VGG16 with CNN in Non-clinical ASD Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Ranjeet Vasant Bidwe,
  • Rashmi Ashtagi,
  • Sashikala Mishra,
  • Simi Bajaj

摘要

In this study, we introduce a new technique based on image processing to predict “autism spectrum disorder” (ASD) images using a combination of transfer learning and VGG16 Convolutional Neural Network (CNN) architecture. Using an extensive dataset of 2540 training pics, 100 validation, and 300 test images, the model showed an accuracy of 71.33%, which is quite impressive. The proposed approach, by leveraging pre-trained weights on a large image dataset and fine-tuning ASD-specific data, holds great promise for effectively screening people with ASD. Conclusions The results indicate that deep learning methods could help to enhance the accuracy of ASD diagnosis and interventions, inherently improve early diagnosis and specialized therapy interventions for patients with ASD. In general, this research sets the stage for better and more individualized treatments for people with ASD. It also sets the ground for future research focusing on increasing the accuracy and robustness of machine learning models that could be used in ASD prediction and management.