Skip to main Communities My dashboard Log in Sign up Amal Jyothi College of Engineering Autonomous Published April 15, 2026 | Version v1 Conference paper Open Deep Learning Based Depression Detection Using MRI Brain Scans Authors/Creators Mariya VB, Amy (Researcher) 1 Paul, Dr. Paulin (Supervisor) 1 Show affiliations 1. Amal Jyothi College of Engineering Description Depression is considered one of the most common mental health issues across the world, which has created a number of challenges for its early detection and treatment. The existing detection methods of depression are largely dependent on psychological assessments, which have resulted in delayed or incorrect detection of depression. The advancements in neu- roimaging techniques, along with the integration of artificial intelligence, have created new possibilities for the detection of depression biomarkers. This paper aims at presenting a deep learning framework for the detection of depression, which utilizes MRI images of the human brain. A CNN model has been employed for training a deep learning framework, which has focused on the detection of abnormalities related to the prefrontal cortex, hippocampus, and amygdala regions of the brain, which are related to emotional regulation. The experimental results have revealed promising classification accuracy of the system, which has outperformed the results of traditional machine learning techniques, making the integration of neuroimaging with deep learning a promising solution for assisting medical professionals with the early detection of depression. Files 19.Amy Mariya (2)-1-4 (1).pdf Files (477.6 kB) Name Size Download all 19.Amy Mariya (2)-1-4 (1).pdf md5:e11baf78f59519fa71e00e0e3fc251ba 477.6 kB Preview Download Additional details Identifiers ISBN 978-93-342-7372-4 23 Views 24 Downloads Show more details All versions This version Views Total views 23 23 Downloads Total downloads 24 24 Data volume Total data volume 12.4 MB 12.4 MB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Keywords and subjects Keywords Depression detection, MRI, deep learning, con- volutional neural networks, neuroimaging, mental health diag- nosis Details DOI DOI Badge DOI 10.5281/zenodo.19588001 Markdown [](https://doi.org/10.5281/zenodo.19588001) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.19588001.svg :target: https://doi.org/10.5281/zenodo.19588001 HTML <a href="https://doi.org/10.5281/zenodo.19588001"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.19588001.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.19588001.svg Target URL https://doi.org/10.5281/zenodo.19588001 Resource type Conference paper Publisher Zenodo Imprint Proceedings of the National Conference on Emerging Computer Applications (NCECA)-2026 7 ed.. Amal Jyothi College of Engineering, Kanjirapally.. ISBN: 978-93-342-7372-4. Conference National Conference on Emerging Computer Applications ((NCECA)-2026), Amal Jyothi College of Engineering, Kanjirapally, 25-03-2026 Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more Copyright Amal Jyothi College of Engineering 2026 Citation Export Technical metadata Created April 15, 2026 Modified April 15, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. Find out more on how we use cookies Accept all cookies Accept only essential cookies