## 1 Introduction
Furthermore, in [29], the authors reviewed machine learning applications for bipolar diagnosis using EEG and MRI data. Research shows that the detection accuracy of bipolar disorder ranges from 58% to 95%, depending on the methodology and dataset used. On the other hand, in [30], multimodal imaging techniques combining structural and functional MRI (sMRI and fMRI) were used to achieve 83.49% accuracy in schizophrenia classification. Even with recent advancements in the detection of BD or SCH from healthy groups, classification of BD and SCH remains a significant challenge. Therefore, studies specifically addressing this issue in the literature are relatively limited. In [25], an ensemble hybrid feature selection method was proposed for the classification of psychological disorders, combining data from structural MRI (sMRI) images and phenotypic records. While the classification accuracy for distinguishing psychiatric disorders from healthy controls reaches 91%, classification accuracy for differentiating between psychological disorders is 78%. One example study addressing this problem is given in [31]. They developed a system using resting-state functional magnetic resonance imaging (rsfMRI) and 1D convolutional neural networks (CNN), achieving an area under the curve (AUC) score of 0.7078 in distinguishing SCH from BD. They employed various machine learning techniques, such as support vector machines (SVM), K-nearest neighbors (KNN), logistic regression (LR), linear discriminant analysis (LDA) and Gaussian naive Bayes (GNB). Building on these advancements, this study aims to further explore deep learning methods for classifying BD and SCH using neuroimaging data. By synthesizing findings from prior research, we aim to contribute to the development of reliable diagnostic tools that enhance clinical decision-making and patient outcomes. Despite these advances, the need for more comprehensive models that can handle large-scale datasets remains, especially in disorders like bipolar disorder and schizophrenia, where neurobiological differences can be subtle and heterogeneous. The objective of this study is to develop and evaluate a novel deep learning framework for detecting and distinguishing bipolar disorder from schizophrenia using structural MRI. The model aims to capture subtle neuroanatomical differences to improve diagnostic accuracy and to aid early intervention. The key contributions can be listed as follows:  
1. Novel Deep Learning Model: A tailored deep learning model optimized for psychiatric MRI data, designed to identify distinct structural features of BD and SCH. Besides its unique design, the proposed model uses sMRI images along with features extracted from Grad-CAM heatmaps to provide insights into model decision-making and to boost performance. The identified regions are demonstrated to play a significant role in classification. Besides, these regions not only emphasize their importance but also allow the model to utilize specific brain areas for improved accuracy. This approach contributes to the overall success of the classification task. 2. Comprehensive Model Evaluation: Rigorous assessment of the model's ability to classify BD and SCH, including accuracy, sensitivity, and generalizability, specific to each subject. Previous research has only characterized structural problems at the group level, with no clear method for making individual diagnoses at the subject level. On the other hand, in this study, both spatial and morphological analyses are evaluated together with classification metrics. 3. Brain Region Insights: Key brain regions and structural abnormalities most relevant for distinguishing the two disorders are identified. Brain region insights are derived from both quantitative performance metrics and qualitative visual observations, offering new insights into their neurobiological underpinnings. This study enhances the role of deep learning in psychiatry, providing an innovative tool for accurate, non-invasive diagnosis.