The most common psychiatric disorder is Major Depressive Disorder (MDD),
commonly known as depression. This article seeks to identify early signs of depression
in college students to avoid major consequences. Students are observed and, if found
stressed with the combination of various emotions, then are sent to the university
counselor for counseling for their good mental health. In this paper, we used deep
learning techniques to detect depression using the clinically approved dataset. With
the help of the suggested model, which combines CNN (Convolutional Neural
Network) and GAN (Generative Adversarial Network) used for transfer learning
strategy and image processing, we have built a system for the early diagnosis of
depression in students. The early diagnosis can create a scope for intervention and
alleviate the worst effects of clinical depression. The accuracy of the CNN models is
75.82 %, while the GAN model is 70.05%.