Depression Level Calculation for Predicting Child Psychometric Retardation using DepressNet Approach through GPU Accelerated Google Cloud Platform

Abstract

From the past decades, depression has become a common threat for everyone. Doctors often ignore the intellectual disability of young children. This happens as a single discovery or as a part of the problem in 2% to 3% of the population. There are many reasons for the widespread syndrome or mental disability.  Among the observed patient data 30% - 50% of cases, cannot be determined by the doctor about the cause despite through examination. The diagnosis mainly depends on a complete personal family medical history, a complete physical examination, and a careful evaluation of the child's development. Being able to deal with these intermediate stages of emotions will be an easier preventive measure. This measure will also help to build a healthy society. There must be a way that does not compromise people's privacy. Human-computer Interaction (HCI) along with Machine Learning algorithms paved the way for finding solution for certain children with depression. One can analyse these issues of depression prediction among children easily with the process of image detection. The goal at the beginning of the project was to create a reliable toolkit for children. Data sets from sources such as AVEC can be used to build real-time Depression prediction systems. There are many ancient systems for recognizing emotions, so this proposed ResNet Algorithm promotes good results when comparing to other detection approaches of depression. However, all this work aims to establish a system based on the analysis of depression. The researchers can rely on this system to make a good model of depression analysis in future and can solves the problem in hand.