Abstract
Depression is a crucial entity of clinical research that imposes challenges to clinicians regarding its diagnosis and correct treatment. Machine learning technologies, are being used successfully for diagnosis of depression, especially using fMRI (functional Magnetic Resonance Imaging). The brain fMRIs are T2* weighted images captured using Blood Oxidation Level Dependent (BOLD) signals and show the functional activity of the brain. BOLD signal maps the activated/functional areas of the brain according the level of oxygen transmitted. Machine Learning and fMRI when taken together provide a tool for clinicians to identify the depressed patients from healthy controls. This paper reviews methodologies of past studies for depression classification using fMRI and machine learning.
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Gupta, S., Vig, R. (2019). Machine Learning Models for Depression Patient Classification Using fMRI: A Study. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11620. Springer, Cham. https://doi.org/10.1007/978-3-030-24296-1_55
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