A comparative study of lung cancer prediction using deep learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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Brac University
2023
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| Online dostop: | http://hdl.handle.net/10361/18698 |
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10361-18698 |
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10361-186982023-07-10T21:03:16Z A comparative study of lung cancer prediction using deep learning Mugdho, Aka Mohammad Bhuiyan, Md. Jawad Hossain Rafin, Tawsif Mustasin Amit, Adib Muhammad Chakrabarty, Amitabha Rasel, Annajiat Alim Department of Computer Science and Engineering, Brac University Hog feature extraction Lung cancer Deep learning ResNet18 DeneNet161 MobileNetV2 ShuffleNet InceptionV3 VGG19 Cognitive learning theory Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 52-54). At the point when cells in the body develop out of control, this is alluded to as cancerous development. Lung cancer is the term used to depict cancer that starts in the lungs. At first in the field, classifier-based approaches are joined with various division calculations to utilize picture acknowledgment to recognize lung cancer nodules. This study found that CT scan images are more reasonable for delivering improved results than other imaging modalities. The use of the images is a piece of chiefly inspecting the CT scanned images that are viewed as informational collections for patients affected by lung cancer. The suggestion of our paper exclusively centers around the execution of concentrating on the calculation’s accuracy in diagnosing lung cancer. Thus, the primary plan of our examination is to utilize examined calculations to conclude which strategy is the most efficient method for detecting lung cancer initially. After training the model we found that Over all accuracy of Resnet-18 is 99.54%, the Overall accuracy of Vgg-19 is 96.35%, The overall accuracy of MobileNet V2 is 98.17%, Dense Net161 is 99.09% and Inception V3 is 98.17%. So we can see that ResNet18 perform better than other train model. Aka Mohammad Mugdho Md. Jawad Hossain Bhuiyan Tawsif Mustasin Rafin Adib Muhammad Amit B. Computer Science 2023-07-10T03:56:33Z 2023-07-10T03:56:33Z 2022 2022-09 Thesis ID 16101249 ID 16301187 ID 18301102 ID 21241062 http://hdl.handle.net/10361/18698 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. 54 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Hog feature extraction Lung cancer Deep learning ResNet18 DeneNet161 MobileNetV2 ShuffleNet InceptionV3 VGG19 Cognitive learning theory Machine learning |
| spellingShingle |
Hog feature extraction Lung cancer Deep learning ResNet18 DeneNet161 MobileNetV2 ShuffleNet InceptionV3 VGG19 Cognitive learning theory Machine learning Mugdho, Aka Mohammad Bhuiyan, Md. Jawad Hossain Rafin, Tawsif Mustasin Amit, Adib Muhammad A comparative study of lung cancer prediction using deep learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Mugdho, Aka Mohammad Bhuiyan, Md. Jawad Hossain Rafin, Tawsif Mustasin Amit, Adib Muhammad |
| format |
Thesis |
| author |
Mugdho, Aka Mohammad Bhuiyan, Md. Jawad Hossain Rafin, Tawsif Mustasin Amit, Adib Muhammad |
| author_sort |
Mugdho, Aka Mohammad |
| title |
A comparative study of lung cancer prediction using deep learning |
| title_short |
A comparative study of lung cancer prediction using deep learning |
| title_full |
A comparative study of lung cancer prediction using deep learning |
| title_fullStr |
A comparative study of lung cancer prediction using deep learning |
| title_full_unstemmed |
A comparative study of lung cancer prediction using deep learning |
| title_sort |
comparative study of lung cancer prediction using deep learning |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/18698 |
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| _version_ |
1814309136249126912 |