Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models
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
2021
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10361-156772022-01-26T10:04:50Z Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models Islam, Tahsin Absar, Shahriar Nasif, S.M. Ali Ijtihad Mridul, Sadman Sakib Islam, MD.Saiful Rahman, Rafeed Department of Computer Science and Engineering, Brac University Covid-19 Respiratory diseases X-ray CT-Scan Deep Neural Network CNN VGG19 Inception v3 MobileNetV2 Resnet-50 Rapid approach Neural networks (Computer science) Machine learning Respiratory agents 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 30-31). The world is going through a severe viral pandemic which is caused by COVID- 19. People infected with this virus, experience severe respiratory illness. The virus spreads through particles of saliva or droplets from an infected person. There are ways of identifying COVID-19 based on the symptoms such as fever, dry cough, tiredness, but these symptoms are similar to other existing viral or respiratory infections. There is no quick approach in diagnosing if a patient is infected or not. To overcome the drawbacks mentioned, a faster diagnosis is needed which leads us to the objective of this study. we intend to construct a diagnostic approach that uses pre-existing data mostly on COVID-19, as well as take datasets from other respiratory diseases. We will apply deep learning models to the acquired datasets enabling us to obtain more accurate and efficient results. We aim to use Deep Neural Network models namely Convolutional Neural Network models (CNN) such as VGG19, Inception v3, MobileNetV2, and ResNet-50. These four models are pre-trained and they classify the CT-Scan images based on the trained learning approaches. The result of each model is compared among the models to get faster and more accurate results. This paper also proposes a "Hybrid" model which is composed of a Convolutional Neural Network (CNN) and a Support Vector Machine (SVM). The Hybrid Model is shallow and just as accurate as the pre-trained models. In light of the exactness of the result and the minimal measure of time needed for image classi cation, we will be able to diagnose more accurately and effectively. Tahsin Islam Shahriar Absar S.M. Ali Ijtihad Nasif Sadman Sakib Mridul B. Computer Science 2021-12-01T04:46:13Z 2021-12-01T04:46:13Z 2021 2021-10 Thesis ID 21141076 ID 17101410 ID 16301054 ID 17101157 http://hdl.handle.net/10361/15677 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. 31 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Covid-19 Respiratory diseases X-ray CT-Scan Deep Neural Network CNN VGG19 Inception v3 MobileNetV2 Resnet-50 Rapid approach Neural networks (Computer science) Machine learning Respiratory agents |
| spellingShingle |
Covid-19 Respiratory diseases X-ray CT-Scan Deep Neural Network CNN VGG19 Inception v3 MobileNetV2 Resnet-50 Rapid approach Neural networks (Computer science) Machine learning Respiratory agents Islam, Tahsin Absar, Shahriar Nasif, S.M. Ali Ijtihad Mridul, Sadman Sakib Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models |
| 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 |
Islam, MD.Saiful |
| author_facet |
Islam, MD.Saiful Islam, Tahsin Absar, Shahriar Nasif, S.M. Ali Ijtihad Mridul, Sadman Sakib |
| format |
Thesis |
| author |
Islam, Tahsin Absar, Shahriar Nasif, S.M. Ali Ijtihad Mridul, Sadman Sakib |
| author_sort |
Islam, Tahsin |
| title |
Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models |
| title_short |
Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models |
| title_full |
Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models |
| title_fullStr |
Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models |
| title_full_unstemmed |
Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models |
| title_sort |
deep neural network models for covid-19 diagnosis from ct-scan, explainability and analysis using trained models |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15677 |
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