An efficient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modeling
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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| 在线阅读: | http://hdl.handle.net/10361/15753 |
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10361-157532022-01-26T10:15:49Z An efficient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modeling Sagor, Mostofa Kamal Jahan, Ishrat Chowdhury, Susmita Ansary, Rubayet Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Lung disease Chest X-ray images Convolution neural network (CNN) Deep learning Transfer learning Diagnostics facilitated by electronics 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. Cataloged from PDF version of thesis. Includes bibliographical references (pages 29-31). Among the most convenient bacteriological assessments for the diagnosis and treatment with several health complications is the chest X-Ray. The World Health Organization (WHO) estimates, for instance, that pneumonic plague induces between 250,000 to 500,000 fatalities annually. Pneumonia and flu are serious challenges towards global health as well as being a source of significant death rates globally. [1]. In X-Ray imaging, it is a common technique to standardize the extracted image reconstruction with usual uniform disciplines taken before the study. Unfortunately, there has been relatively little study on several separate lung disease monitoring, including X-Ray picture analysis and poorly labelled repositories. Our paper suggests an effective approach for the detection of lung disease trained on automated chest X-ray images that could encourage radiologists in their moral choice. Besides, with a weighted binary classifier, a particular technique is also deployed that will optimally leverage the weighted predictions from optimal deep neural networks such as InceptionV3, VGG16 and ResNet50. In addition to the existing, transfer learning, along with more rigorous academic training and testing sets, is used to fine-tune deep neural networks to achieve higher internal processes. In comparison, 88.14 percent test accuracy was obtained with the final proposed weighted binary classifier, where other models give us about 76.91 percent average accuracy. For a brief recurring diagnosis, the legally prescribed procedure may also be used which may increase the course of the same condition for physicians. For a prompt diagnosis of pneumonia, the suggested approach should be used and can improve the diagnosis process for health practitioners. Mostofa Kamal Sagor Ishrat Jahan Susmita Chowdhury Rubayet Ansary B. Computer Science 2021-12-26T04:37:41Z 2021-12-26T04:37:41Z 2021 2021-01 Thesis ID 17301106 ID 17101458 ID 17101025 ID 20241050 http://hdl.handle.net/10361/15753 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 |
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Institutional Repository |
| language |
English |
| topic |
Lung disease Chest X-ray images Convolution neural network (CNN) Deep learning Transfer learning Diagnostics facilitated by electronics Deep learning |
| spellingShingle |
Lung disease Chest X-ray images Convolution neural network (CNN) Deep learning Transfer learning Diagnostics facilitated by electronics Deep learning Sagor, Mostofa Kamal Jahan, Ishrat Chowdhury, Susmita Ansary, Rubayet An efficient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modeling |
| 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 Sagor, Mostofa Kamal Jahan, Ishrat Chowdhury, Susmita Ansary, Rubayet |
| format |
Thesis |
| author |
Sagor, Mostofa Kamal Jahan, Ishrat Chowdhury, Susmita Ansary, Rubayet |
| author_sort |
Sagor, Mostofa Kamal |
| title |
An efficient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modeling |
| title_short |
An efficient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modeling |
| title_full |
An efficient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modeling |
| title_fullStr |
An efficient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modeling |
| title_full_unstemmed |
An efficient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modeling |
| title_sort |
efficient deep learning approach for detecting lung disease from chest x-ray images using transfer learning and ensemble modeling |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15753 |
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