X-Ray classification to detect COVID-19 using ensemble model
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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| Online toegang: | http://hdl.handle.net/10361/15537 |
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10361-155372022-01-26T10:19:56Z X-Ray classification to detect COVID-19 using ensemble model Solaiman, Ishmam Ahmed Sanjana, Tasnim Islam Sobhan, Samila Maria, Tanzila Sultana Rahman, Md Khalilur Department of Computer Science and Engineering, Brac University Pneumonia Coronavirus Deep learning X-Rays Convolutional Neural Network Ensemble model Transfer learning CAD Neural networks (Computer science) COVID-19 (Disease) 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 26-28). Diagnosis with X-Rays and other forms of medical images has soared to new heights as an alternative visual Covid infection detector. Radiographic images, primarily CT scans and X-Rays images play massive roles in assisting radiologists to detect and analyse severe medical conditions. Computer-Aided Diagnosis (CAD) systems are used successfully to detect diseases such as tuberculosis, pneumonia and other common diseases from chest X-ray images. CNNs have been widely adopted by many studies and achieved laudible results in the eld of medical image diagno- sis, having attained state-of-art performance by training on labeled data.This paper aims to propose an Ensemble model using a combination of deep CNN architectures, which are Xception, InceptionResnetV2, VGG19, DenseNet-201 and NasNetLarge, that can aid in the diagnosis of various diseases using image processing and arti - cial intelligence algorithms to quickly and accurately identify COVID-19 and other coronary diseases from X-Rays to stop the rapid transmission of the virus. In our experiment, we have used classi ers for the Xception model, VGG19, and Inception- Resnet model. We have compiled a CXR dataset from various open datasets. The compiled dataset was lacking 1000 images for viral pneumonia in comparison with Covid-19 and Normal CXRs, We used image augmentation and focal loss to com- pensate for the unbalanced data and introduce more variation. After implementing the focal loss function, we were able to get better results. Moreover, we implemented transfer learning on these models using ImageNet weights. Finally, we obtained a training accuracy of 92% to 94% across all models. Our Accuracy of the Ensemble Model was 96.25%. Ishmam Ahmed Solaiman Tasnim Islam Sanjana Samila Sobhan Tanzila Sultana Maria B. Computer Science 2021-10-25T06:46:52Z 2021-10-25T06:46:52Z 2021 2021-06 Thesis ID 19341012 ID 19341011 ID 17141018 ID 17141004 http://hdl.handle.net/10361/15537 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. 28 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Pneumonia Coronavirus Deep learning X-Rays Convolutional Neural Network Ensemble model Transfer learning CAD Neural networks (Computer science) COVID-19 (Disease) |
| spellingShingle |
Pneumonia Coronavirus Deep learning X-Rays Convolutional Neural Network Ensemble model Transfer learning CAD Neural networks (Computer science) COVID-19 (Disease) Solaiman, Ishmam Ahmed Sanjana, Tasnim Islam Sobhan, Samila Maria, Tanzila Sultana X-Ray classification to detect COVID-19 using ensemble model |
| 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 |
Rahman, Md Khalilur |
| author_facet |
Rahman, Md Khalilur Solaiman, Ishmam Ahmed Sanjana, Tasnim Islam Sobhan, Samila Maria, Tanzila Sultana |
| format |
Thesis |
| author |
Solaiman, Ishmam Ahmed Sanjana, Tasnim Islam Sobhan, Samila Maria, Tanzila Sultana |
| author_sort |
Solaiman, Ishmam Ahmed |
| title |
X-Ray classification to detect COVID-19 using ensemble model |
| title_short |
X-Ray classification to detect COVID-19 using ensemble model |
| title_full |
X-Ray classification to detect COVID-19 using ensemble model |
| title_fullStr |
X-Ray classification to detect COVID-19 using ensemble model |
| title_full_unstemmed |
X-Ray classification to detect COVID-19 using ensemble model |
| title_sort |
x-ray classification to detect covid-19 using ensemble model |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15537 |
| work_keys_str_mv |
AT solaimanishmamahmed xrayclassificationtodetectcovid19usingensemblemodel AT sanjanatasnimislam xrayclassificationtodetectcovid19usingensemblemodel AT sobhansamila xrayclassificationtodetectcovid19usingensemblemodel AT mariatanzilasultana xrayclassificationtodetectcovid19usingensemblemodel |
| _version_ |
1814308967612940288 |