Detection of multiple sclerosis using deep learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
| Main Authors: | , , |
|---|---|
| מחברים אחרים: | |
| פורמט: | Thesis |
| שפה: | English |
| יצא לאור: |
Brac University
2021
|
| נושאים: | |
| גישה מקוונת: | http://hdl.handle.net/10361/15737 |
| id |
10361-15737 |
|---|---|
| record_format |
dspace |
| spelling |
10361-157372022-01-26T10:21:53Z Detection of multiple sclerosis using deep learning Jannat, Sabila Al Hoque, Tanjina Supti, Nafisa Alam Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Magnetic Resonance Imaging(MRI) Machine Learning Multiple Scle- rosis(MS) 3D Magnetic Resonance Imaging White Matter Lesion Detection Deep Learning Convolutional Neural Network(CNN) Fluid-attenuated inversion recovery (FLAIR) Data Augmentation Image Processing Machine Learning 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 21-23). Accurate detection of white matter lesions in 3D Magnetic Resonance Images (MRIs) of patients with Multiple Sclerosis is essential for diagnosis and treatment evaluation of MS. It is strenuous for the optimal treatment of the disease to detect early MS and estimate its progression. In this study, we propose efficient Multiple Sclerosis detection techniques to improve the performance of a supervised machine learning algorithm and classify the progression of the disease. Detection of MS lesions become more intricate due to the presence of unbalanced data with a very small number of lesions pixel. Our pipeline is evaluated on MS patients data from the Laboratory of Imaging Technologies. Fluid-attenuated inversion recovery (FLAIR) series are incorporated to introduce a faster system alongside maintaining readability and accuracy. Our approach is based on convolutional neural networks (CNN). We have trained the model using transfer learning and used softmax as an activation function to classify the progression of the disease. Our results significantly show the effectiveness of the usage of MRI of MS lesions. Experiments on 30 patients and 100 healthy brain MRIs can accurately predict disease progression. Manual detection of lesions by clinical experts is complicated and time-consuming as a large amount of MRI data is required to analyze. We analyze the accuracy of the proposed model on the dataset. Our approach exhibits a significant accuracy rate of up to 98.24%. Sabila Al Jannat Tanjina Hoque Nafisa Alam Supti B. Computer Science 2021-12-22T06:31:04Z 2021-12-22T06:31:04Z 2021 2021-01 Thesis ID 17101302 ID 17101129 ID 16201083 http://hdl.handle.net/10361/15737 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. 23 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Magnetic Resonance Imaging(MRI) Machine Learning Multiple Scle- rosis(MS) 3D Magnetic Resonance Imaging White Matter Lesion Detection Deep Learning Convolutional Neural Network(CNN) Fluid-attenuated inversion recovery (FLAIR) Data Augmentation Image Processing Machine Learning |
| spellingShingle |
Magnetic Resonance Imaging(MRI) Machine Learning Multiple Scle- rosis(MS) 3D Magnetic Resonance Imaging White Matter Lesion Detection Deep Learning Convolutional Neural Network(CNN) Fluid-attenuated inversion recovery (FLAIR) Data Augmentation Image Processing Machine Learning Jannat, Sabila Al Hoque, Tanjina Supti, Nafisa Alam Detection of multiple sclerosis using deep 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 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Jannat, Sabila Al Hoque, Tanjina Supti, Nafisa Alam |
| format |
Thesis |
| author |
Jannat, Sabila Al Hoque, Tanjina Supti, Nafisa Alam |
| author_sort |
Jannat, Sabila Al |
| title |
Detection of multiple sclerosis using deep learning |
| title_short |
Detection of multiple sclerosis using deep learning |
| title_full |
Detection of multiple sclerosis using deep learning |
| title_fullStr |
Detection of multiple sclerosis using deep learning |
| title_full_unstemmed |
Detection of multiple sclerosis using deep learning |
| title_sort |
detection of multiple sclerosis using deep learning |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15737 |
| work_keys_str_mv |
AT jannatsabilaal detectionofmultiplesclerosisusingdeeplearning AT hoquetanjina detectionofmultiplesclerosisusingdeeplearning AT suptinafisaalam detectionofmultiplesclerosisusingdeeplearning |
| _version_ |
1814309571223617536 |