<scp>MTT</scp> : Multi‐Tier Transformer for Early Diagnosis of Autism Spectrum Disorder and Language Disorder in Infants

ABSTRACT Current diagnosis of autism spectrum disorder (ASD) and developmental language/reading disorder (DLD/DD) relies predominantly on subjective behavioral assessments, this underscoring the urgent need for objective biomarkers to enable early intervention. This study proposes a Multi‐Tier Transformer (MTT) model for early identification of ASD and DLD/DD using resting‐state EEG baseline power values. To address severe class imbalance, we augmented the dataset using the SMOTE method. The MTT architecture integrates a Feature‐Embedding layer, a Feature‐Attention mechanism that dynamically weights multi‐spectral inputs, and a dual‐attention encoding block comprising both self‐attention and cross‐attention to enhance contextual representation learning from limited samples. Transfer learning was further employed to improve robustness by pre‐training on augmented data and fine‐tuning on original samples. Evaluated on clinical infant EEG data, the proposed MTT achieved an accuracy of 0.91 (95% CI: 0.89–0.93), recall of 0.89, and AUC of 0.97, significantly outperforming the state‐of‐the‐art FT‐transformer ( p = 0.00091). The results indicate that MTT provides a robust and interpretable deep learning tool for auxiliary diagnosis of neurodevelopmental disorders in infancy.