A Hybrid Cnn-Lstm-Based Rnn Model for Classification of Schizophrenia (Global Journal of Sciences, 2025)
Schizophrenia is a mental health condition that affects the behavior and thought  process of a person. With proper treatment, such as medication and therapy, many  people with this medical condition are able to manage their symptoms and live  meaningful lives. Thus, there is a need for early diagnosis of schizophrenia. However,  due to the wide range of symptoms displayed by different schizophrenic patients,  accurately identifying schizophrenia can proof challenging. Different conventional  methods to try to classify schizophrenia are applied in many instances. Nevertheless,  all aspects of the condition could not be sufficiently addressed by any of the  conventional classification techniques. Researchers are currently testing the  application of deep learning approaches for improved schizophrenia categorization. In  this study, we present a novel hybrid RNN model that uses both CNN and LSTM  technology for use in the classification of schizophrenia. The twelve layered model  was developed using a total of 28 samples as input data. The dataset consisted of EEG  signal recordings from 14 healthy controls and 14 schizophrenic patients. After  training and testing, an accuracy of 79.35% was recorded by the model. This model  has the potential to enable early detection of schizophrenia thereby leading to more  optimized treatment and management methods. The study describes a new twelve layered CNN-LSTM hybrid framework for identifying schizophrenia employing EEG  signals. Trained on data from 14 patients and 14 healthy controls, the framework  obtained 79.35% accuracy, highlighting deep learning's promise for early success  in schizophrenia detection.
Keywords: Schizophrenia; Convolutional; Neural Network; Recurrent Neural; Network; Long Short-Term; Memory and Electroencephalogram