  Schizophrenia (SZ) is a complex mental disorder that necessitates accurate
and timely diagnosis for effective treatment. Traditional methods for SZ
classification often struggle to capture transient EEG features and face high
computational complexity. This study proposes a convolutional autoencoder (CAE)
to address these challenges by reducing dimensionality and computational
complexity. Additionally, we introduce a novel approach utilizing spectral
scalograms (SS) combined with EfficientNet (ENB) architectures. The SS,
obtained through continuous wavelet transform, reveals temporal and spectral
information of EEG signals, aiding in the identification of transient features.
ENB models, through transfer learning (TL), extract discriminative features and
improve SZ classification accuracy. Experimental evaluation on a comprehensive
dataset demonstrates the efficacy of our approach, achieving a five-fold mean
cross-validation accuracy of 98.5\% using CAE with a soft voting classifier and
99\% employing SS with the ENB7 model. These results suggest the potential of
our methods to enhance SZ diagnosis, surpassing traditional deep learning (DL)
and TL techniques. By leveraging CAE and ENBs, this research offers a robust
framework for objective SZ classification, promoting early intervention and
improved patient outcomes.
