dc.contributor.advisor | Mohsin, Abu S.M. | |
dc.contributor.author | Sharma, Tanmoyee | |
dc.contributor.author | Tabassum, Zaharat | |
dc.contributor.author | Banik, Ritu | |
dc.contributor.author | Rahman, S.M.Arifur | |
dc.date.accessioned | 2021-10-06T03:31:25Z | |
dc.date.available | 2021-10-06T03:31:25Z | |
dc.date.copyright | 2021 | |
dc.date.issued | 2021 | |
dc.identifier.other | ID 17121035 | |
dc.identifier.other | ID 16221014 | |
dc.identifier.other | ID 16221003 | |
dc.identifier.other | ID 16221002 | |
dc.identifier.uri | http://hdl.handle.net/10361/15142 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021 | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 68-73). | |
dc.description.abstract | 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. | en_US |
dc.description.statementofresponsibility | Tanmoyee Sharma | |
dc.description.statementofresponsibility | Zaharat Tabassum | |
dc.description.statementofresponsibility | Ritu Banik | |
dc.description.statementofresponsibility | S. M. Arifur Rahman | |
dc.format.extent | 73 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | 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. | |
dc.subject | Image segmentation | en_US |
dc.subject | U-Net++ | en_US |
dc.subject | Optical images | en_US |
dc.subject | Deep learning architecture | en_US |
dc.subject.lcsh | Machine learning | |
dc.subject.lcsh | Artificial intelligence | |
dc.title | Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Electrical and Electronic Engineering, Brac University | |
dc.description.degree | B. Electrical and Electronic Engineering | |