Non-clinical Covid19 diagnosis on CT-scan, Chest X-ray, and respiratory patterns using deep-learning and signal processing
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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Brac University
2023
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| Acesso em linha: | http://hdl.handle.net/10361/19392 |
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10361-193922023-08-23T04:45:20Z Non-clinical Covid19 diagnosis on CT-scan, Chest X-ray, and respiratory patterns using deep-learning and signal processing Tasnim, Sadia Sarker, Sukarna Bhoumik, Partha Al Maruf, K.M. Abdullah Rahman Hasib, MD Mahfuzur Hossain, Dr. Muhammad Iqbal Department of Computer Science and Engineering, Brac University Covid-19 CNN VGG16 ResNet50 ResNet101 Prediction Detection Cognitive learning theory (Deep learning) Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-43). Despite various preventative measures and therapies, the COVID-19 pandemic has exposed a number of weaknesses and vulnerabilities in global health systems, particularly in low and middle-income countries that may have less developed healthcare infrastructure and fewer resources to devote to public health. These countries have often been hit hardest by the pandemic, with higher rates of infection and death compared to more developed countries. More than 15 million deaths were reported nationwide over the first two years of the pandemic.In our thesis, we propose a novel, non-clinical method for quickly identifying COVID-19 using deep learning and signal processing techniques. This approach is based on the analysis of CT scans, chest X-rays, and respiratory patterns, and utilizes datasets containing images and audio recordings from both infected and healthy individuals. Our model is able to identify COVID-19 almost accurately using all four of these elements, making it more effec tive than other current models that only use one or two of these parameters. We believe that a non-invasive diagnostic approach could help to identify more cases of COVID-19, particularly in resource-limited settings where traditional diagnostic methods may be less accessible. As the virus continues to evolve,this method has the potential to slow the spread of the virus by enabling earlier detection and isolation of infected individuals. In addition, by providing a faster and more efficient means of diagnosis, this method can help to alleviate the burden on healthcare systems, which have been overwhelmed by the pandemic in many parts of the world. Sadia Tasnim Sukarna Sarker Partha Bhoumik K.M. Abdullah Al Maruf MD Mahfuzur Rahman Hasib B. Computer Science 2023-08-14T04:19:22Z 2023-08-14T04:19:22Z 2023 2023-01 Thesis ID: 19101526 ID: 19301201 ID: 19101415 ID: 19101487 ID: 22341069 http://hdl.handle.net/10361/19392 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. 43 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Covid-19 CNN VGG16 ResNet50 ResNet101 Prediction Detection Cognitive learning theory (Deep learning) Machine learning |
| spellingShingle |
Covid-19 CNN VGG16 ResNet50 ResNet101 Prediction Detection Cognitive learning theory (Deep learning) Machine learning Tasnim, Sadia Sarker, Sukarna Bhoumik, Partha Al Maruf, K.M. Abdullah Rahman Hasib, MD Mahfuzur Non-clinical Covid19 diagnosis on CT-scan, Chest X-ray, and respiratory patterns using deep-learning and signal processing |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Hossain, Dr. Muhammad Iqbal |
| author_facet |
Hossain, Dr. Muhammad Iqbal Tasnim, Sadia Sarker, Sukarna Bhoumik, Partha Al Maruf, K.M. Abdullah Rahman Hasib, MD Mahfuzur |
| format |
Thesis |
| author |
Tasnim, Sadia Sarker, Sukarna Bhoumik, Partha Al Maruf, K.M. Abdullah Rahman Hasib, MD Mahfuzur |
| author_sort |
Tasnim, Sadia |
| title |
Non-clinical Covid19 diagnosis on CT-scan, Chest X-ray, and respiratory patterns using deep-learning and signal processing |
| title_short |
Non-clinical Covid19 diagnosis on CT-scan, Chest X-ray, and respiratory patterns using deep-learning and signal processing |
| title_full |
Non-clinical Covid19 diagnosis on CT-scan, Chest X-ray, and respiratory patterns using deep-learning and signal processing |
| title_fullStr |
Non-clinical Covid19 diagnosis on CT-scan, Chest X-ray, and respiratory patterns using deep-learning and signal processing |
| title_full_unstemmed |
Non-clinical Covid19 diagnosis on CT-scan, Chest X-ray, and respiratory patterns using deep-learning and signal processing |
| title_sort |
non-clinical covid19 diagnosis on ct-scan, chest x-ray, and respiratory patterns using deep-learning and signal processing |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/19392 |
| work_keys_str_mv |
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