Retraction of: Scientific Reports https://doi.org/10.1038/s41598-025-96014-6, published online 08 April 2025
The Editor has retracted and removed this publication. Concerns were raised regarding the use of images of minors from a non-curated dataset. The dataset is reported to contain images of children with ASD. However, the images appear to have been collected from the internet without any documented clinical history or confirmation of an actual ASD diagnosis. Additionally, there is no documented ethical oversight, or consent of the children included in the dataset or their parents and legal guardians.
In light of these ethical concerns and the reliance on unverifiable data, the Editor and Publisher no longer have confidence in the reliability of this article.
Authors Mahmood A. Mahmood and Leila Jamel have stated that the authors disagree with this retraction.
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Abstract
The global population contains a substantial number of individuals who experience autism spectrum disorder, thus requiring immediate identification to enable successful intervention approaches. The authors assess the detection of autism-related learning difficulties in children by evaluating deep learning models that use transfer learning methods along with fine-tuning methods. Using autism spectrum disorder (ASD) diagnosed child RGB images data, researchers evaluated six prevalent deep learning structures: DenseNet201, ResNet152, VGG16, VGG19, MobileNetV2, and EfficientNet-B0. ResNet152 reached the highest accuracy rate of 89% when functioning independently. This paper develops a hybrid deep-learning model by integrating ResNet152 with Vision Transformers (ViT) to achieve better classification performance. The ViT-ResNet152 model’s convolutional and transformer processing elements worked together to improve the accuracy of the diagnosis to 91.33% and make it better at finding different cases of autism spectrum disorder (ASD).The research outcomes demonstrate that AI tools show promise for delivering highly precise and standardized methods to detect ASD at an early stage. Future research needs to include multiple data types as well as extend dataset variability while optimizing hybrid architecture systems to elevate diagnostic forecasting. The incorporation of artificial intelligence in ASD evaluation services holds promise to transform early therapy approaches, which leads to better results for autistic children all around the globe.
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Details
1 Department of Information Systems, College of Computer and Information Sciences, Jouf University, 72341, Sakaka, Aljouf, Kingdom of Saudi Arabia (ROR: https://ror.org/02zsyt821) (GRID: grid.440748.b) (ISNI: 0000 0004 1756 6705); Department of Information Systems and Technology, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt (ROR: https://ror.org/03q21mh05) (GRID: grid.7776.1) (ISNI: 0000 0004 0639 9286)
2 Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Kingdom of Saudi Arabia (ROR: https://ror.org/05b0cyh02) (GRID: grid.449346.8) (ISNI: 0000 0004 0501 7602)
3 Department of Computer Science, College of Computer and Information Sciences, Jouf University, 72341, Sakaka, Aljouf, Kingdom of Saudi Arabia (ROR: https://ror.org/02zsyt821) (GRID: grid.440748.b) (ISNI: 0000 0004 1756 6705); Department of Computer Science, Faculty of Computers and Information, Menoufia University, 32511, Shebin Elkom, Egypt (ROR: https://ror.org/05sjrb944) (GRID: grid.411775.1) (ISNI: 0000 0004 0621 4712)