An optimized EEG-based hybrid deep learning framework for schizophrenia detection

Schizophrenia (SCZ) is a severe and persistent mental health condition that profoundly affects individuals, their families, and broader communities. With rising global incidence and symptoms overlapping with disorders like bipolar illness, many remain unaware of its presence in daily life. Early diagnosis enables timely intervention, improving treatment outcomes and symptom management. Traditional machine learning approaches for schizophrenia detection rely on feature extraction and selection before classification. Deep learning (DL), renowned for modeling complex hierarchical patterns, accelerates the development of precise and objective diagnostic tools. Therefore, this research proposed a novel hybrid deep-learning approach for diagnosing Schizophrenia at an early stage. In this study, we developed an innovative framework employing the Mutation-enhanced Archimedes Optimization (MAO) algorithm to improve EEG preprocessing and signal clarity. Spatial and temporal features from multi-channel EEG data are analyzed through a hybrid deep learning approach, which mainly combines a Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) network. The proposed framework integrated an MAO into the CNN-GRU-MAO model, which enhances the capability to detect schizophrenia. A dual-objective optimization technique bootup detection accuracy and noise reduction, enhancing the overall effectiveness of the model. The experimental results demonstrated excellent performance and outperformed traditional approaches in terms of accuracy, precision, recall, F1-score, and specificity 98.41%, 98.13%, 98.87%, 98.49%, and 97.78% respectively. The MAO technique also evaluates signal integrity, enhancing Signal-to-Noise Ratio (SNR) and Signal-to-Interference Ratio (SIR) while reducing artifact contamination. This study highlights the ability of the MAO method in EEG preprocessing for schizophrenia detection. Integrating a deep learning framework with innovative optimization methods offers a transformative mechanism for improving mental health diagnostics via neurophysiological signal analysis.