Deep Learning for Early Detection of Depression and Anxiety: A Comparative Study of CNN, LSTM, and RNN Models

Depressive and anxiety disorders have been ranked among the most important public health problems and have been a concern for all age groups, reducing the quality of life of individuals. Commonly used diagnostic methods are not very effective in early detection since they rely on self-reporting, which is a subjective measure, and are often not available in remote areas. In this situation, AI, particularly deep learning, offers the best tools for mental health conditions to be measured accurately and detected early, accurately, and non-invasively. The present work is mainly concerned with the detection of deep learning algorithms, specifically Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN), which will help in detecting depression and anxiety at an early stage. It takes the wide variety of AI-based methods and organizes them into precise groups, examines actual case studies for each model, and conducts comparative studies by assessing their performance, applied datasets, techniques, and the results they produced. By way of a systematic comparison of real-world cases, the authors of the paper have pointed out that CNNs are highly accurate with image data, LSTMs are very good with text and audio input in a sequence, and RNNs are the best when it comes to temporal behavioral changes, as they allow the most flexibility in the modeling. Another section on hybrid and ensemble techniques has provided further evidence of the future trend of joining models for better diagnostic accuracy. The comparison between different approaches enables us to recommend the use of LSTM-based systems as the most flexible and context-sensitive solution for contemporary applications. The findings indicate that there is a large gap to be filled by the integration of AI-based systems into mental health detection processes so that the diseases are caught in an incipient stage, especially when working in resource-constrained areas.