ArticleComputational intelligence and neuroscience2022
Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 134 citations in OpenAlex.
- Retraction Note: Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis.BMC psychiatry · 2025Article
- Data Mining-Based Model for Computer-Aided Diagnosis of Autism and Gelotophobia: Mixed Methods Deep Learning Approach.JMIR formative research · 2025Article
- Hybrid CNN and random forest model with late fusion for detection of autism spectrum disorder in Toddlers.MethodsX · 2025Article
- Application of artificial intelligence in modern healthcare for diagnosis of autism spectrum disorder.Frontiers in medicine · 2025Article
- Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review.Cognitive neurodynamics · 2024Review
- Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks.Healthcare technology letters · 2024Article
- Autism spectrum disorders detection based on multi-task transformer neural network.BMC neuroscience · 2024Article
- A new diagnostic autism spectrum disorder (DASD) strategy using ensemble diagnosis methodology based on blood tests.Health information science and systems · 2023Article
- Article
- Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features.Diagnostics (Basel, Switzerland) · 2023Article
- An Approach to Binary Classification of Alzheimer's Disease Using LSTM.Bioengineering (Basel, Switzerland) · 2023Article
- Disentangling disorder-specific variation is key for precision psychiatry in autism.Frontiers in behavioral neuroscience · 2023Article
- Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework.Computational intelligence and neuroscience · 2023Article
- Retracted: Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms.Computational intelligence and neuroscience · 2023Article
- A face image classification method of autistic children based on the two-phase transfer learning.Frontiers in psychology · 2023Article
- A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder.PloS one · 2023Article
- RETRACTED: Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach.Bioengineering (Basel, Switzerland) · 2022Article
Corrections and comments
- Retraction · 2023-10-04Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum disorder (ASD) is a neurological disease correlated with brain growth that later impacts the physical impression of the face. Children with ASD have dissimilar facial landmarks, which set them noticeably apart from typically developed (TD) children. Novelty of the proposed research is to design a system that is based on autism spectrum disorder detection on social media and face recognition. To identify such landmarks, deep learning techniques may be used, but they require a precise technology for extracting and producing the proper patterns of the face features. This study assists communities and psychiatrists in experimentally detecting autism based on facial features, by using an uncomplicated web application based on a deep learning system, that is, a convolutional neural network with transfer learning and the flask framework. Xception, Visual Geometry Group Network (VGG19), and NASNETMobile are the pretrained models that were used for the classification task. The dataset that was used to test these models was collected from the Kaggle platform and consisted of 2,940 face images. Standard evaluation metrics such as accuracy, specificity, and sensitivity were used to evaluate the results of the three deep learning models. The Xception model achieved the highest accuracy result of 91%, followed by VGG19 (80%) and NASNETMobile (78%).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.