Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021
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Brac University
2021
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| Sarrera elektronikoa: | http://hdl.handle.net/10361/15142 |
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10361-15142 |
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10361-151422021-10-06T21:01:28Z Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture Sharma, Tanmoyee Tabassum, Zaharat Banik, Ritu Rahman, S.M.Arifur Mohsin, Abu S.M. Department of Electrical and Electronic Engineering, Brac University Image segmentation U-Net++ Optical images Deep learning architecture Machine learning Artificial intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021 Cataloged from PDF version of thesis. Includes bibliographical references (pages 68-73). Image segmentation is a fundamental section of the current healthcare system to segment and detect diseases such as disease of the lung (pneumothorax), cancer, diabetic retinopathy, dengue, malaria, heart disease, Alzheimer’s disease, liver disease, rheumatoid arthritis, and so on. Lung segmentation, Cell segmentation, Brain segmentation, Liver segmentation are some of the popular medical segmentations. In this study, we worked on two different types of images x-ray image and optical image for lung (Pneumothorax) and cell (nucleus) image segmentation. For both cases, we employed U-Net++ for image classification and segmentation to detect and identify Pneumothorax or cell nuclei. Additionally, we incorporated several image recognition models U-Net, ResNet34, Inception V3 within U-Net++ architecture and investigated which model provides better accuracy with minimum loss. The findings of our study will be not only beneficial for clinicians for accurate diagnosis but also will be helpful to lessen diagnostic limitations. Tanmoyee Sharma Zaharat Tabassum Ritu Banik S. M. Arifur Rahman B. Electrical and Electronic Engineering 2021-10-06T03:31:25Z 2021-10-06T03:31:25Z 2021 2021 Thesis ID 17121035 ID 16221014 ID 16221003 ID 16221002 http://hdl.handle.net/10361/15142 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. 73 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Image segmentation U-Net++ Optical images Deep learning architecture Machine learning Artificial intelligence |
| spellingShingle |
Image segmentation U-Net++ Optical images Deep learning architecture Machine learning Artificial intelligence Sharma, Tanmoyee Tabassum, Zaharat Banik, Ritu Rahman, S.M.Arifur Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021 |
| author2 |
Mohsin, Abu S.M. |
| author_facet |
Mohsin, Abu S.M. Sharma, Tanmoyee Tabassum, Zaharat Banik, Ritu Rahman, S.M.Arifur |
| format |
Thesis |
| author |
Sharma, Tanmoyee Tabassum, Zaharat Banik, Ritu Rahman, S.M.Arifur |
| author_sort |
Sharma, Tanmoyee |
| title |
Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture |
| title_short |
Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture |
| title_full |
Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture |
| title_fullStr |
Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture |
| title_full_unstemmed |
Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture |
| title_sort |
image segmentation of x-ray and optical images using u-net/unet++ based deep learning architecture |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15142 |
| work_keys_str_mv |
AT sharmatanmoyee imagesegmentationofxrayandopticalimagesusingunetunetbaseddeeplearningarchitecture AT tabassumzaharat imagesegmentationofxrayandopticalimagesusingunetunetbaseddeeplearningarchitecture AT banikritu imagesegmentationofxrayandopticalimagesusingunetunetbaseddeeplearningarchitecture AT rahmansmarifur imagesegmentationofxrayandopticalimagesusingunetunetbaseddeeplearningarchitecture |
| _version_ |
1814306992944054272 |