Leveraging unsupervised segmentation for semi-supervised renal calculi and carcinoma segmentation and classification
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
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Brac University
2024
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| Online Zugang: | http://hdl.handle.net/10361/23965 |
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10361-239652024-10-01T08:56:11Z Leveraging unsupervised segmentation for semi-supervised renal calculi and carcinoma segmentation and classification Faruk, Farhan Alam, H.M. Sarwer Alam, Md. Golam Rabiul Rahman, Rafeed Department of Computer Science and Engineering, Brac University CT image Carcinoma Renal calculi Segmentation Imaging systems in medicine. Diagnostic imaging--Digital techniques. Urinary organs--Calculi. Kidneys--Calculi Renal cell carcinoma. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 31-34). Indeed it became crucial to develop an AI-driven system for detecting Renal illnesses spontaneously due to a healthcare issue of Renal failure. The global shortage of nephrologists is the core reason for this. This research concerns two crucial diseases: Renal Calculi and Carcinoma by using semi-supervised localization and unsupervised segmentation of a total 5477 CT images of axial point of view in order to use it in commonly used models. The gathered CT images are prepared by resizing and balancing via augmentation and the analysis shows that the mean color distribution of images was the same across all classes. We have used YoloV8 for kidney localization, quick shift and label merge for segmentation. Also, we manually annotated all the images with the supervision of a medical expert. Furthermore, we compare both datasets using VGG19, AlexNet, ResNet50. We got better output in Semi- Supervised localized and unsupervised segmented images rather than the annotated images via experts in terms of classification. We believe that reducing and focusing on the region of interest can easily achieve good output in commonly used models which will be very time efficient and cost effective as well. Farhan Faruk H.M. Sarwer Alam B.Sc. in Computer Science 2024-09-04T04:41:36Z 2024-09-04T04:41:36Z ©2024 2024-06 Thesis ID 20301137 ID 20301224 http://hdl.handle.net/10361/23965 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 |
CT image Carcinoma Renal calculi Segmentation Imaging systems in medicine. Diagnostic imaging--Digital techniques. Urinary organs--Calculi. Kidneys--Calculi Renal cell carcinoma. |
| spellingShingle |
CT image Carcinoma Renal calculi Segmentation Imaging systems in medicine. Diagnostic imaging--Digital techniques. Urinary organs--Calculi. Kidneys--Calculi Renal cell carcinoma. Faruk, Farhan Alam, H.M. Sarwer Leveraging unsupervised segmentation for semi-supervised renal calculi and carcinoma segmentation and classification |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Faruk, Farhan Alam, H.M. Sarwer |
| format |
Thesis |
| author |
Faruk, Farhan Alam, H.M. Sarwer |
| author_sort |
Faruk, Farhan |
| title |
Leveraging unsupervised segmentation for semi-supervised renal calculi and carcinoma segmentation and classification |
| title_short |
Leveraging unsupervised segmentation for semi-supervised renal calculi and carcinoma segmentation and classification |
| title_full |
Leveraging unsupervised segmentation for semi-supervised renal calculi and carcinoma segmentation and classification |
| title_fullStr |
Leveraging unsupervised segmentation for semi-supervised renal calculi and carcinoma segmentation and classification |
| title_full_unstemmed |
Leveraging unsupervised segmentation for semi-supervised renal calculi and carcinoma segmentation and classification |
| title_sort |
leveraging unsupervised segmentation for semi-supervised renal calculi and carcinoma segmentation and classification |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23965 |
| work_keys_str_mv |
AT farukfarhan leveragingunsupervisedsegmentationforsemisupervisedrenalcalculiandcarcinomasegmentationandclassification AT alamhmsarwer leveragingunsupervisedsegmentationforsemisupervisedrenalcalculiandcarcinomasegmentationandclassification |
| _version_ |
1814308175533309952 |