Approved research

Classification of Major Depressive Disorder from Brain fMRI Scans using Machine Learning Techniques

Lay summary

Our research project aims to develop a better understanding of Major Depressive Disorder (MDD) and improve early detection and diagnosis through advanced machine learning techniques. We want to figure out which machine learning method works best among Support Vector Machine, LightGBM, and Convolutional Neural Networks (CNN), and CNN with Transfer Learning.

The scientific rationale behind our project is straightforward: by harnessing the power of machine learning, we can analyze large amounts of data to identify subtle patterns and indicators of MDD. This could lead to earlier detection and intervention, ultimately improving outcomes for individuals struggling with this condition.

The project duration is estimated to span 6-8 months, depending on the complexity of the analysis and the availability of data.

The potential public health impact of our research is significant. By improving early detection and diagnosis of MDD, we can help healthcare professionals intervene sooner, potentially preventing the worsening of symptoms and reducing the overall burden of the disorder on individuals and society. Additionally, our findings could inform the development of more effective screening tools and treatment strategies for MDD, leading to better outcomes and quality of life for those affected.