Deep Learning based Depression Estimation using Facial Expressions

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Deep Learning based Depression Estimation using Facial Expressions

DOI: 10.46335/IJIES.2024.9.2.1

Authors : Chhaya Nayak , Dr. Sachin Patel

Abstract –The serious mental health condition of depression may greatly impact a person's daily life. Chronic sorrow, lack of interest in once-enjoyed activities, weariness, changes in food or sleeping patterns, difficulties focusing, feelings of hopelessness, and other symptoms are typical symptoms of depression. Effective treatments are readily available to help control symptoms and enhance the quality of life. Deep learning, a subset of machine learning, has been utilized to identify and analyze brain activity patterns that may indicate depression. Deep learning models use artificial neural networks to learn from and make predictions on large data sets. These models can accurately identify patterns and relationships within complex data sets, including brain activity data. Researchers have used deep learning models to analyze EEG (electroencephalogram) data from depressed patients and healthy controls. By comparing the brain activity patterns between these two groups, deep learning models have been able to identify unique features specific to depressed individuals. This analysis has also led to the development of predictive models that can accurately classify individuals as depressed or not based on their brain activity patterns.While deep learning is still in its early stages of development for mental health applications, the potential for improving depression diagnosis and treatment is promising. As research progresses, deep learning models could become a valuable tool in aiding mental health professionals in identifying and treating depression.