Multiple classification of childhood and juvenile autism spectrum disorder from ABIDE-II sMRI using BN-GO-CNN

OBJECTIVES: Autism spectrum disorder (ASD) shows heterogeneous neurodevelopmental patterns across childhood, juvenile development, and adulthood, so binary MRI classification may not fully capture age-and sex-related variation. This study proposes a proof-of-concept framework for multiple classification using ABIDE-II T1-weighted structural MRI (sMRI). METHODS: After image-quality screening, sMRI slices were processed using Canny edge detection, brain-region cropping, 224 × 224 resizing, and augmentation. Images were organized into sex-based four-class, age-based four-class, and combined age-and-sex eight-class datasets, separating child, young, juvenile, adult, male, and female groups. A Batch-Normalized Grid-Optimized CNN (BN-GO-CNN) was developed to learn structural representations directly from preprocessed sMRI, with grid search used to select key CNN hyperparameters. The model was compared with VGG16, DenseNet201, MobileNetV2, and EfficientNet-B0 using identical image partitions. RESULTS: BN-GO-CNN achieved validation accuracies of 67.0 %, 64.0 %, and 60.5 % for sex-based, age-based, and combined age-and-sex classification, respectively. These outcomes remained above chance but showed limited class separability. CONCLUSIONS: The modest performance suggests that label-related sMRI differences are subtle, supporting this framework as a preliminary test of CNN-assisted sMRI classification for future research use rather than a clinically reliable ASD diagnostic system.