## 6 Conclusions  
This study introduces an advanced deep-learning framework to address the diagnostic challenges posed by bipolar disorder (BD) and schizophrenia (SCH) using structural MRI data. The integration of ResNet-50, Grad-CAM, ElasticNet-based feature selection, and Bayesian-optimized deep neural networks (BO-DNN), which leverage explainability to extract features has demonstrated improved MRI analysis performance in classifying these psychological disorders compared to traditional machine learning methods.  
The Grad-CAM technique enhanced model interpretability by highlighting diagnostically relevant brain regions, such as the prefrontal cortex, anterior cingulate cortex, and  
Figure 11: Sample Grad-CAM result.  
hippocamps. After highlighting the important regions for the decision-making process and extracting the features from these regions, the model's performance in classifying BD and SCH was comprehensively evaluated using both spatial and morphological analysis. In this way, we obtained improved classification outcomes, as evidenced by the significant gains in accuracy and F1-scores. The success of the BO-DNN model underscores the importance of advanced optimization in achieving robust generalizability, making it a promising candidate for clinical applications.  
Beyond improved diagnostic accuracy, the framework proposed in this study provides comprehensive insights, obtained from both quantitative and qualitative analyses, into key brain regions most relevant for differentiating these disorders. Furthermore, this novel approach overcomes the limitations of traditional methods, such as subjective assessments and reliance on overt symptoms. The results align with existing literature on the neuroanatomical distinctions of BD and SCH while providing additional insights into the structural underpinnings of these disorders.  
However, there are certain limitations in this study. The reliance on a single open-source dataset, while effective for initial validation, may limit the generalizability of the findings. Future studies should focus on larger, multi-site datasets to ensure broader applicability. Additionally, incorporating longitudinal data could capture the progression of neuroanatomical changes, further enhancing the model's clinical utility.  
In conclusion, this study advances the role of deep learning in psychiatric neuroimaging, offering a robust, interpretable, and scalable framework to classify BD and SCH. The proposed methodology and deep learning-based framework improve diagnostic accuracy and provide insights into key neurobiological markers, paving the way for more objective, non-invasive, and early diagnostic tools. With continued refinement and validation, this approach holds significant potential to transform psychiatric diagnostics and support personalized treatment strategies.  
Conceptualization O.T.B., C.C. and M.A.S.; methodology, O.T.B. and C.C.; software, O.T.B. and M.A.S.; formal analysis, C.C. and O.T.B.; investigation, O.T.B., C.C. and M.A.S.; writing--original draft preparation, C.C. and O.T.B.; writing--review and editing, O.T.B., C.C. and M.A.S.; visualization, O.T.B. and C.C.; supervision M.A.S. All authors have read and agreed to the published version of the manuscript.  
This project is supported by TUSEB under grant number 12157.  
Not applicable.  
Open-source dataset collected from the University of California Los Angeles (UCLA) Consortium of Neuropsychiatric Phenomics LA5c study13.  
The authors declare no conflicts of interest.