Early ADHD detection is vital for children’s mental health. Traditional methods are subjective and inconsistent. This paper presents an ensemble learning approach using a Parallel Attention-Based BiLSTM (PABiLSTM) model on EEG data. Spectrograms, fractal dimensions, and recurrence plots processed via ResNet-50 enhance feature extraction and classification accuracy. Exhaustive experiments on two datasets results in the accuracy drops of 98.91% and 99.10% on respective datasets. Although deep…
