Bipolar Disorder (BD) is a psychiatric condition diagnosed by repetitive cycles of hypomania and depression. Since diagnosing BD relies on subjective behavioral assessments over a long period, a solid diagnosis based on objective criteria is not straightforward. The current study responded to the described obstacle by proposing a hybrid GAN-CNN model to diagnose BD from 3-D structural MRI Images (sMRI). The novelty of this study stems from diagnosing BD from sMRI samples rather than conventional datasets such as functional MRI (fMRI), electroencephalography (EEG), and behavioral symptoms while removing the data insufficiency usually encountered when dealing with sMRI samples. The impact of various augmentation ratios is also tested using 5-fold cross-validation. Based on the results, this study obtains an accuracy rate of 75.8%, a sensitivity of 60.3%, and a specificity of 82.5%, which are 3-5% higher than prior work while utilizing less than 6% sample counts. Next, it is demonstrated that a 2- D layer-based GAN generator can effectively reproduce complex 3D brain samples, a more straightforward technique than manual image processing. Lastly, the optimum augmentation threshold for the current study using 172 sMRI samples is 50%, showing the applicability of the described method for larger sMRI datasets. In conclusion, it is established that data augmentation using GAN improves the accuracy of the CNN classifier using sMRI samples, thus developing more reliable decision support systems to assist practitioners in identifying BD patients more reliably and in a shorter period

翻译：双相情感障碍（BD）是一种以反复发作的轻躁狂与抑郁周期为特征的精神疾病。由于BD诊断依赖于长期的主观行为评估，基于客观标准的可靠诊断并非易事。本研究针对上述难题提出了一种混合GAN-CNN模型，用于从三维结构磁共振图像（sMRI）中诊断BD。与传统数据集（如功能磁共振成像fMRI、脑电图EEG及行为症状）不同，本研究的创新之处在于利用sMRI样本进行BD诊断，同时解决sMRI样本处理中常见的数据不足问题。通过5折交叉验证测试了不同增广比例的影响。结果显示，本研究获得了75.8%的准确率、60.3%的灵敏度和82.5%的特异度，在仅使用不到6%样本量的情况下，这三项指标均比先前研究提高了3-5%。此外，研究表明基于二维层的GAN生成器可有效复现复杂的三维脑样本，该方法比手动图像处理更简洁。最后，针对172个sMRI样本，本研究的最优增广阈值为50%，表明所述方法适用于更大规模的sMRI数据集。结论证实，使用GAN进行数据增广可提升基于sMRI样本的CNN分类器精度，从而开发出更可靠的决策支持系统，辅助临床医生更准确、更快捷地识别BD患者。