Detection of Autism Spectrum Disorder (ASD) Using Lightweight Ensemble CNN Based on Facial Images for Improved Diagnostic Accuracy

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects how people talk to each other and act. The fact that ASD is becoming more common and that diagnosing it can be difficult means that early detection is important for improving treatment outcomes. This study's goal is to use lightweight ensemble Convolutional Neural Networks (CNN) to make it easier to classify ASD from facial photos. The study looks at different CNN architectures, like MobileNetV2 and EfficientNet variations, to find the best model for diagnosing ASD quickly and accurately. The method involves training and testing five lightweight CNN models on a set of facial photos. We use pre-processing methods like scaling and data augmentation to help the model learn better. The study tests how well ensemble CNN models work by combining predictions from different architectures using averaging and voting methods. We use important performance metrics like accuracy, precision, recall, and F1-score to see how well each model works. The results show that the best balance between accuracy and computational efficiency is achieved by combining MobileNetV2 and EfficientNetB0. This combination achieves an accuracy of 0.8299, a precision of 0.8514, a recall of 0.8182, and an F1_score of 0.8344. Other models, like ResNet50 combined with EfficientNetB0, have higher precision but lower recall, making them less useful for finding all ASD cases. This study was also compared with other researchers, and the proposed study was found to have greater accuracy than other researchers. The results show that ensemble CNN models can significantly improve the accuracy of classifying ASD compared to single CNNs. This study shows that lightweight ensemble CNN models are good at finding ASD in pictures of people's faces. The method is fast and can be used on devices with limited processing power, making it a good way to find ASD early in both clinical and real-world settings.