ArticleBrain sciences2022
Identification of Autism in Children Using Static Facial Features and Deep Neural Networks.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systemic review of facial expression recognition (FER) in stroke: diagnosis and emerging applications in rehabilitation.Frontiers in neurologyPooled it
- DHCA-Net: A Novel Dual-Stream Hierarchical Channel Attention Network for Explainable Autism Spectrum Disorder Detection From Facial Images.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Article
- NeuroMimicNet: A Multimodal Neuromorphic Framework for Early Screening and Cognitive Pattern Recognition in Children with Autism.Journal of multidisciplinary healthcare · 2026Article
- Artificial Intelligence in Autism Spectrum Disorder Diagnosis: A Scoping Review of Face, Voice, and Text Analysis Methods.Health science reports · 2025Review
- Harnessing YOLOv11 for Enhanced Detection of Typical Autism Spectrum Disorder Behaviors Through Body Movements.Diagnostics (Basel, Switzerland) · 2025Article
- Robust Autism Spectrum Disorder Screening Based on Facial Images (For Disability Diagnosis): A Domain-Adaptive Deep Ensemble Approach.Diagnostics (Basel, Switzerland) · 2025Article
- Leveraging hybrid model of ConvNextBase and LightGBM for early ASD detection via eye-gaze analysis.MethodsX · 2025Article
- The reanimation of pseudoscience in machine learning and its ethical repercussions.Patterns (New York, N.Y.) · 2024Review
- Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder.Molecular psychiatry · 2023Review
- Article
- A face image classification method of autistic children based on the two-phase transfer learning.Frontiers in psychology · 2023Article
- FER-PCVT: Facial Expression Recognition with Patch-Convolutional Vision Transformer for Stroke Patients.Brain sciences · 2022Article
- RETRACTED: Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach.Bioengineering (Basel, Switzerland) · 2022Article
- Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2022Article
- Investigation of Eye-Tracking Scan Path as a Biomarker for Autism Screening Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2022Article
- Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models.Computational and mathematical methods in medicine · 2022Article
- Deep Learning-Based Mental Health Model on Primary and Secondary School Students' Quality Cultivation.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder (ASD) is a complicated neurological developmental disorder that manifests itself in a variety of ways. The child diagnosed with ASD and their parents' daily lives can be dramatically improved with early diagnosis and appropriate medical intervention. The applicability of static features extracted from autistic children's face photographs as a biomarker to distinguish them from typically developing children is investigated in this study paper. We used five pre-trained CNN models: MobileNet, Xception, EfficientNetB0, EfficientNetB1, and EfficientNetB2 as feature extractors and a DNN model as a binary classifier to identify autism in children accurately. We used a publicly available dataset to train the suggested models, which consisted of face pictures of children diagnosed with autism and controls classed as autistic and non-autistic. The Xception model outperformed the others, with an AUC of 96.63%, a sensitivity of 88.46%, and an NPV of 88%. EfficientNetB0 produced a consistent prediction score of 59% for autistic and non-autistic groups with a 95% confidence level.
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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.