Integrating CNN-GRU-BiLSTM for Robust Schizophrenia Detection using Deep Learning

Abstract

Early detection of schizophrenia is critical but challenging due to its complex presentation. In order to interpret EEG data, this work proposes a hybrid deep learning model that combines Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), and Bidirectional Long Short-Term Memory (BiLSTM) networks with an attention mechanism. The model captures spatial, temporal, and contextual dependencies, achieving a classification accuracy of 99.30%, outperforming existing methods like CNN-LSTM and CNN-BiLSTM. By integrating spatial feature extraction, temporal dynamics, and attention for interpretability, the model offers robust, efficient, and transparent diagnostics. Verified on TUH EEG Corpus and CHB-MIT EEG datasets, it demonstrates the potential of deep learning models based on EEG for accurate and scalable early schizophrenia identification, opening the door for revolutionary uses in mental health diagnostics.