## 1 Introduction  
Mental health, a state of well-being characterized by the ability to cope with life's stressors, work productively, and contribute to society, is significantly influenced by biological, psychological, and social factors [1; 2; 3]. It plays a pivotal role in thought, emotion, behavior, and decision-making. Conversely, mental disorders (also known as mental illnesses), characterized by disturbances in thought, emotion, behavior, or cognition, can lead to distress and impaired functioning. Among the various mental disorders, bipolar disorder (BD) and schizophrenia (SCH) are prevalent, particularly in younger populations. While BD exhibits higher rates in individuals aged 18-29, SCH typically manifests in late adolescence or early adulthood [4; 5]. Despite its lower prevalence compared to BD, SCH carries a significantly higher mortality risk [6]. Current diagnostic methods for BD and SCH primarily rely on subjective clinical assessments, interviews, and self-reported symptoms [7]. These approaches, while valuable, are prone to variability in understanding the patient's subjective experience. Diagnosis can often be inconsistent due to overlapping symptoms between disorders, misreporting by patients, or clinicians' interpretation. Additionally, many symptoms may not manifest clearly until the disorder has significantly progressed, complicating early detection. Structural magnetic resonance imaging (sMRI), while capable of identifying anatomical changes in these disorders--such as reduced gray matter volume in schizophrenia or alterations in cortical thickness in bipolar disorder--often lacks the sensitivity to capture subtle patterns [8; 9; 10]. Thus, it necessitates advanced methods for more objective, early, and accurate diagnosis. As a result, current approaches fall short of providing objective, early, and accurate diagnosis, underscoring the need for advanced methods that can integrate neuroimaging data more effectively. Machine learning, particularly deep learning, offers a promising approach for analyzing complex neuroimaging data [11; 12]. Early successes in applying machine learning to psychiatric disorders, such as autism spectrum disorder (ASD) and Alzheimer's disease, have demonstrated its potential for identifying disease-specific brain patterns [13; 14]. In BD and SCH, machine learning has shown promise in identifying structural brain differences. Studies have utilized MRI data to detect abnormalities in regions related to emotion regulation and gray matter reductions, two well-established neurobiological markers of these disorders. Unlike traditional statistical methods, deep learning (DL) algorithms can automatically learn and extract intricate patterns from large datasets [15]. In recent years, deep learning has been successfully applied in various fields, including radiology and neurology, demonstrating its potential in psychiatry [16; 17; 18]. DL models, such as convolutional neural networks (CNNs), have shown an ability to detect subtle structural changes in the brain associated with psychiatric disorders [19; 20]. This technology offers a new way to analyze imaging data, potentially overcoming the limitations of current diagnostic methods. Moreover, DL approaches can provide greater classification accuracy, enabling more reliable differentiation between disorders like BD and SCH [21]. The application of deep learning has further advanced the field, with CNNs outperforming traditional machine learning approaches in identifying more subtle structural differences that are often missed by classical methods. Some recent studies have successfully applied DL to distinguish between Alzheimer's disease, bipolar disorder, schizophrenia, and healthy controls based on structural MRI data, achieving improved accuracy and generalizability [22; 23; 24; 25; 26; 27]. In [28], functional near-infrared spectroscopy (fNIRS) data were utilized with CNNs for bipolar detection, and they obtained an average classification accuracy of 70%.