A deep learning approach for prediction of ADHD using brain structure

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Podrobná bibliografie
Hlavní autoři:	Siam, Shafin Ahad, Datta, Durjoy, Hossain, S.M.Kawsar, Nobel, MD.Nishat Ahmed, Ahamed, Ashik
Další autoři:	Sadeque, Farig Yousuf
Médium:	Diplomová práce
Jazyk:	English
Vydáno:	Brac University 2024
Témata:	Deep learning Convolutional neural network Ensemble learning Diagnosis Artificial intelligence Data mining Neural networks (Computer science) Ensemble learning (Machine learning) > Industrial applications
On-line přístup:	http://hdl.handle.net/10361/23530

id	10361-23530
record_format	dspace
spelling	10361-235302024-06-24T21:04:38Z A deep learning approach for prediction of ADHD using brain structure Siam, Shafin Ahad Datta, Durjoy Hossain, S.M.Kawsar Nobel, MD.Nishat Ahmed Ahamed, Ashik Sadeque, Farig Yousuf Department of Computer Science and Engineering, Brac University Deep learning Convolutional neural network Ensemble learning Diagnosis Artificial intelligence Data mining Neural networks (Computer science) Ensemble learning (Machine learning)--Industrial applications Artificial intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 46-48). Attention deficit hyperactivity disorder (ADHD) is a complex condition affecting the brain’s neurodevelopmental processes. Prompt treatment and accurate diagnosis can alter neural connections and improve symptoms. This article focuses on the classification of MRI scans of individuals with ADHD and those without the disorder using deep learning algorithms. Pre-trained models like VGG-16, RegNet-50, and DenseNet-121 were used, along with non-pretrained models like CNN and convolutional LSTM. VGG16 is known for its intricate architecture, while CNNs have undergone significant advancements, resulting in improved classification accuracy. The convolutional LSTM model, a novel integration of CNNs and LSTM networks, was used to predict ADHD based on anatomical data from the brain. The results showed that only the VGG-16, CNN, and convolutional LSTM models exhibited superior accuracy. Ensemble learning was used to create an ensemble model, with convolutional LSTM having 93% accuracy, CNN having 95% accuracy, and ensemble learning having 97% accuracy. Overall, ensemble learning had the highest accuracy, indicating the need for a model that can detect ADHD with better accuracy. Shafin Ahad Siam Durjoy Datta S.M.Kawsar Hossain MD.Nishat Ahmed Nobel Ashik Ahamed B.Sc in Computer Science 2024-06-24T03:45:47Z 2024-06-24T03:45:47Z ©2023 2023-09 Thesis ID 23341080 ID 19301208 ID 19301221 ID 19301133 ID 19301123 http://hdl.handle.net/10361/23530 en 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. 59 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep learning Convolutional neural network Ensemble learning Diagnosis Artificial intelligence Data mining Neural networks (Computer science) Ensemble learning (Machine learning)--Industrial applications Artificial intelligence
spellingShingle	Deep learning Convolutional neural network Ensemble learning Diagnosis Artificial intelligence Data mining Neural networks (Computer science) Ensemble learning (Machine learning)--Industrial applications Artificial intelligence Siam, Shafin Ahad Datta, Durjoy Hossain, S.M.Kawsar Nobel, MD.Nishat Ahmed Ahamed, Ashik A deep learning approach for prediction of ADHD using brain structure
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Sadeque, Farig Yousuf
author_facet	Sadeque, Farig Yousuf Siam, Shafin Ahad Datta, Durjoy Hossain, S.M.Kawsar Nobel, MD.Nishat Ahmed Ahamed, Ashik
format	Thesis
author	Siam, Shafin Ahad Datta, Durjoy Hossain, S.M.Kawsar Nobel, MD.Nishat Ahmed Ahamed, Ashik
author_sort	Siam, Shafin Ahad
title	A deep learning approach for prediction of ADHD using brain structure
title_short	A deep learning approach for prediction of ADHD using brain structure
title_full	A deep learning approach for prediction of ADHD using brain structure
title_fullStr	A deep learning approach for prediction of ADHD using brain structure
title_full_unstemmed	A deep learning approach for prediction of ADHD using brain structure
title_sort	deep learning approach for prediction of adhd using brain structure
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/23530
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_version_	1814309157058117632

A deep learning approach for prediction of ADHD using brain structure

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