  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
