ArticleBrain sciences2021
Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage.
Article in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Methods in Early Detection of Autism Spectrum Disorder: A DSM-5 Criterion-Based Systematic Review.Autism research : official journal of the International Society for Autism Research · 2026Pooled 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
- A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data.Bioengineering (Basel, Switzerland) · 2025Review
- Deep Learning-Powered Down Syndrome Detection Using Facial Images.Life (Basel, Switzerland) · 2025Article
- Data Mining-Based Model for Computer-Aided Diagnosis of Autism and Gelotophobia: Mixed Methods Deep Learning Approach.JMIR formative research · 2025Article
- AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.Patterns (New York, N.Y.) · 2025Review
- Application of artificial intelligence in modern healthcare for diagnosis of autism spectrum disorder.Frontiers in medicine · 2025Article
- The association between music therapy and mental health in Chinese university students: a moderated mediation model of self-evaluation and gender.Frontiers in psychology · 2025Article
- Diagnosing autism spectrum disorder based on eye tracking technology using deep learning models.Frontiers in medicine · 2025Article
- Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology.Diagnostics (Basel, Switzerland) · 2024Article
- ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis.Scientific reports · 2024Article
- Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study.Applied sciences (Basel, Switzerland) · 2024Article
- Can micro-expressions be used as a biomarker for autism spectrum disorder?Frontiers in neuroinformatics · 2024Article
- Utilizing deep learning models in an intelligent eye-tracking system for autism spectrum disorder diagnosis.Frontiers in medicine · 2024Article
- Multi-Slice Generation sMRI and fMRI for Autism Spectrum Disorder Diagnosis Using 3D-CNN and Vision Transformers.Brain sciences · 2023Article
- Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features.Diagnostics (Basel, Switzerland) · 2023Article
- A face image classification method of autistic children based on the two-phase transfer learning.Frontiers in psychology · 2023Article
- RETRACTED: Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach.Bioengineering (Basel, Switzerland) · 2022Article
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9 authors.
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Abstract
Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important markers that can be used to identify ASD by analyzing facial features, eye contact, and so on. In this work, an improved transfer-learning-based autism face recognition framework is proposed to identify kids with ASD in the early stages more precisely. Therefore, we have collected face images of children with ASD from the Kaggle data repository, and various machine learning and deep learning classifiers and other transfer-learning-based pre-trained models were applied. We observed that our improved MobileNet-V1 model demonstrates the best accuracy of 90.67% and the lowest 9.33% value of both fall-out and miss rate compared to the other classifiers and pre-trained models. Furthermore, this classifier is used to identify different ASD groups investigating only autism image data using k-means clustering technique. Thus, the improved MobileNet-V1 model showed the highest accuracy (92.10%) for k = 2 autism sub-types. We hope this model will be useful for physicians to detect autistic children more explicitly at the early stage.
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