Autism Spectrum Disorder Detection from facial images using Deep Convolutional Neural Networks

Abstract

Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition for which early screening is essential to support timely intervention. Conventional diagnostic procedures are often time-consuming and resource-intensive, highlighting the need for accessible and cost-effective screening approaches. This study investigates the use of deep learning–based facial image analysis as a supportive tool for ASD screening rather than as a diagnostic solution. A comparative evaluation of three state-of-the-art convolutional neural network architectures EfficientNetB0, MobileNetV2, and ResNet50 is conducted using a unified experimental framework based on transfer learning and fine-tuning. Experiments are performed on a publicly available Autistic Children Facial Image Dataset, with an additional verification step to prevent duplicate images in order to enhance generalization and reduce potential bias. All models are trained and evaluated under identical conditions to ensure a fair comparison. Experimental results indicate that EfficientNetB0 achieves the best overall performance, reaching a test accuracy of 88%, while maintaining a favorable balance between model complexity and generalization ability.