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Depression Is A Common And Serious Mental Health Disorder That Affects Millions Of People Worldwide. Early Detection And Diagnosis Of Depression Are Crucial For Effective Treatment And Management Of The Condition. The Use Of Machine Learning Algorithms, Such As Convolutional Neural Networks (CNNs), Has Shown Promising Results In Detecting Depression From Various Data Sources, Including Audio, Text, And Images. This Paper Proposes A CNN-based Approach For Depression Detection Using Facial Images. The Proposed System Involves The Use Of A Pre-trained CNN Model, Such As VGG-16, To Extract Features From Facial Images. The Extracted Features Are Then Used To Train A Support Vector Machine (SVM) Classifier To Detect Depression.

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