## 2 Literature Review
### AI-Driven Innovations in Schizophrenia Diagnosis: Applying DL and EEG Data
[52] presented a CNN-based approach in their study, highlighting the ability of DL to process complex EEG signals and improve diagnostic accuracy. Their work highlights how CNNs can model non-linear relationships in EEG data, going beyond traditional classification techniques. This method provides scalable and more accurate tools for the early detection of schizophrenia, enhancing the potential of CNNs in neuropsychiatric diagnosis, as the proposed method had a 92% accuracy. A novel hybrid DL model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with deep CNNs was addressed in the work of [PERSON] et al. [53]. The model was developed to process and analyze Multi-Scale Spectral Transformation (MSST) spectral images derived from EEG signals. This approach exploits the strengths of both Bi-LSTM networks and CNNs. The Bi-LSTM component is adept at capturing temporal dependencies and patterns from sequential EEG data, while the CNN component excels at extracting features from spectral images. The suggested MSST-Bi-CNN approach yielded classification results with an accuracy of 84.42%. [PERSON] et al. [54] presented SchizoNET, a state-of-the-art deep neural network model designed for schizophrenia detection using EEG signals. Their study explores the integration of advanced time-frequency analysis with DL techniques. The SchizoNET model employs a Margenau-Hill time-frequency distribution (TFD) as a foundational preprocessing step. This distribution provides a detailed representation of the EEG signal in both time and frequency domains, capturing intricate spectral features that are crucial for accurate classification. The processed TFD images are then fed into a deep neural network designed to identify patterns associated with schizophrenia. The proposed method was applied on three datasets, resulting in the following accuracies: 97.4% (on dataset 1 [55]), 99.74% (on dataset 2 [55]), and 96.35% (on dataset 3 [56]). An autoencoder-based model designed to enhance the classification accuracy of EEG signal classification in schizophrenia diagnosis is presented in the research of [PERSON] et al. [57]. The approach improves feature extraction from EEG data by addressing the challenges associated with noisy and high-dimensional signal data. By refining the representation of EEG features through unsupervised learning, the model improves subsequent classification performance. The efficiency of the suggested technique was verifiedusing the three previously mentioned publicly available EEG datasets for schizophrenia. Classification accuracy attained was 99.989%, 95.012%, and 96.69%, respectively. Another study that focuses on feature extraction is proposed by [PERSON] et al. [58] and highlights the application of residual deep networks (ResNets). The study uses ResNets to capture complex patterns and features in EEG data by utilizing residual learning techniques, which allow the network to learn and refine features more efficiently. They demonstrate that their approach significantly improves classification performance compared to conventional methods by leveraging the deep residual architecture to handle the complexities and variabilities inherent in EEG signals. Among the reported classifiers, the SVM classifier, with an accuracy of 99.23%, specificity of 99.02%, precision of 99.36%, and F1-score of 99.36%, exhibited the best classification performance in the majority of instances. In comparison, the accuracy, specificity, precision, and F1-score values of the DL classifier were 97.48%, 97.90%, 98.58%, and 97.88%, respectively. The proposed examples of AI-driven models are proving to be transformative in the detection and diagnosis of schizophrenia through the analysis of EEG data. From CNN-based models, like SchizoGoogLeNet, to hybrid approaches combining Bi-LSTMs and CNNs, these advancements offer significant improvements in diagnostic accuracy and scalability.