A deep learning approach to depression detection based on Convolutional Neural Networks and Transfer Learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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Brac University
2022
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| Accès en ligne: | http://hdl.handle.net/10361/17576 |
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10361-175762022-11-16T21:01:38Z A deep learning approach to depression detection based on Convolutional Neural Networks and Transfer Learning Sarmi, Kaniz Fatima Rahman, Shaikh Mahmudur Sultana, Nusrat Jahan Anzoom Shanto, Khandaker MD. Asef Parvez, Dr. Mohammad Zavid Department of Computer Science and Engineering, Brac University Electroencephalogram (EEG) Major Depressive Disorder (MDD) Brain Signal Analysis Transfer Learning Models VGG16 ResNet152 Xception MobileNetV2 Machine Learning Machine Learning Electroencephalography Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-48). Depression and mental health issues (stress, nervousness, panic attacks, anxiety attacks etc.) are nowadays a major issue in the whole world. It is a common cause of mental illness that has been linked to an increased risk of dying young. Especially in our country, mental health is an issue which most of the families do not want to give as much attention as it is supposed to get and because of that so many people who are suffering from Major Depressive Disorder (MDD) are often helpless. Currently there are numerous ways to detect depression by various methods. For example: emotion recognition, social media records, analyzing daily routine with the help of machine learning and many more. This paper aims to detect depression by implementing various deep learning/ transfer learning models (for example: VGG16, Xception, ResNet152, MobileNetV2 etc.) using EEG brain signals to discover the model that provides the highest level of accuracy for our data type. In addition, we want to analyze why the particular model performs better and what might be the cases to make a model perform better to propose a model so that this method of modeling can be used in most cases for detecting depression and model improvement. Furthermore, we have made a custom model which gives the most accuracy (99.75%). We are successful at bringing the highest accuracy among the existing models which were implemented by us. For this reason, we are analyzing the EEG brain signal data of several healthy and MDD patients. We believe that this research will aid in the development of innovative strategies for building models and early identification of depression in our daily lives. Kaniz Fatima Sarmi Shaikh Mahmudur Rahman Nusrat Jahan Sultana Khandaker MD. Asef Anzoom Shanto B. Computer Science 2022-11-16T04:50:33Z 2022-11-16T04:50:33Z 2021 2021-10 Thesis ID: 17102040 ID: 17101338 ID: 17101331 ID: 17101248 http://hdl.handle.net/10361/17576 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 48 Pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Electroencephalogram (EEG) Major Depressive Disorder (MDD) Brain Signal Analysis Transfer Learning Models VGG16 ResNet152 Xception MobileNetV2 Machine Learning Machine Learning Electroencephalography Neural networks (Computer science) |
| spellingShingle |
Electroencephalogram (EEG) Major Depressive Disorder (MDD) Brain Signal Analysis Transfer Learning Models VGG16 ResNet152 Xception MobileNetV2 Machine Learning Machine Learning Electroencephalography Neural networks (Computer science) Sarmi, Kaniz Fatima Rahman, Shaikh Mahmudur Sultana, Nusrat Jahan Anzoom Shanto, Khandaker MD. Asef A deep learning approach to depression detection based on Convolutional Neural Networks and Transfer Learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Parvez, Dr. Mohammad Zavid |
| author_facet |
Parvez, Dr. Mohammad Zavid Sarmi, Kaniz Fatima Rahman, Shaikh Mahmudur Sultana, Nusrat Jahan Anzoom Shanto, Khandaker MD. Asef |
| format |
Thesis |
| author |
Sarmi, Kaniz Fatima Rahman, Shaikh Mahmudur Sultana, Nusrat Jahan Anzoom Shanto, Khandaker MD. Asef |
| author_sort |
Sarmi, Kaniz Fatima |
| title |
A deep learning approach to depression detection based on Convolutional Neural Networks and Transfer Learning |
| title_short |
A deep learning approach to depression detection based on Convolutional Neural Networks and Transfer Learning |
| title_full |
A deep learning approach to depression detection based on Convolutional Neural Networks and Transfer Learning |
| title_fullStr |
A deep learning approach to depression detection based on Convolutional Neural Networks and Transfer Learning |
| title_full_unstemmed |
A deep learning approach to depression detection based on Convolutional Neural Networks and Transfer Learning |
| title_sort |
deep learning approach to depression detection based on convolutional neural networks and transfer learning |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17576 |
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