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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models

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    21141076, 17101410, 16301054, 17101157_CSE.pdf (1.299Mb)
    Date
    2021-10
    Publisher
    Brac University
    Author
    Islam, Tahsin
    Absar, Shahriar
    Nasif, S.M. Ali Ijtihad
    Mridul, Sadman Sakib
    Metadata
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    URI
    http://hdl.handle.net/10361/15677
    Abstract
    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.
    Keywords
    Covid-19; Respiratory diseases; X-ray; CT-Scan; Deep Neural Network; CNN; VGG19; Inception v3; MobileNetV2; Resnet-50; Rapid approach
     
    LC Subject Headings
    Neural networks (Computer science); Machine learning; Respiratory agents
     
    Description
    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).
    Department
    Department of Computer Science and Engineering, Brac University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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