Approved research

A convolutional neural network classifier for gender classification

Lay summary

Mental illness cause the heaviest economic pressures to the society among all types of diseases and the patients suffer enormous pain. But the diagnose of the psychiatric disorders is merely based on the subjective diagnose of psychiatrists. Researchers want to develop an diagnose technique which is more objective and accuracy using MRI which is entirely harmless to the patients. One outstanding technique is deep-learning algorithm which is a kind of artificial intelligence (AI) method. We want to distinguish the patients and normal people using the deep-learning algorithm built on MRI. But the proper parameters of the deep-learning model are largely undetermined, so build a deep-learning model for distinguish mental illness patients is much infeasible. As physiology gender is a much robust characteristic of human, which is also an dichotomous variable. We want to built the deep-learning algorithm for gender classification. And then, We would try to transfer the model on mental illness. The project duration is about 12 mouth including data accumulation, preprocessing, building deep-learning model and testing the model. The present work aims to solve some fundamental necessities for the clinical diagnosis of mental illness, and it may facilitate the development of treatment for mental illness and reduce the economic pressures of society and the suffering of patients.