Depression Detection using Deep Learning

Abstract

Major depression disorder (MDD) is the single greatest cause of disability and morbidity that affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on Deep learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders. In this study, we review popular Deep learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression. Clinical depression increases disability, and reduced functionality also leads to suicide due to the depression. Nowadays, social media information provides potential information regarding mental behavior due to the frequent interaction of the users with their social media platforms in their daily life. Even though the depression diagnosis systems have proven an effective treatment for depressed patients, misdiagnosing people is critical due to the lack of intelligent systems and resources. Most depression detection models analyze the textual content in the social media posts too early to detect the depression level. Owing to the increased uncertainties in people’s feelings, the depression detection system encounters challenges in recognizing the risk level of depression.