Kidney Disease detection and classification from CT Images using Watershed Segmentation and 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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| Ngôn ngữ: | English |
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Brac University
2023
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/18371 |
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10361-18371 |
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10361-183712023-05-30T21:01:43Z Kidney Disease detection and classification from CT Images using Watershed Segmentation and Deep Learning. Hossain, Mohammad Sakib Hassan, S.M. Nazmul rahaman, Md. Nakib Al-Amin, Mohammad Hossain, Rakib Hossain, Dr. Muhammad Iqbal Khondaker, Ms. Arnisha Department of Computer Science and Engineering, Brac University Watershed Algorithm InceptionV3 Squeezenet ResNet50 VGG19 Transfer Learning EAnet Kidney diseases -- Diagnosis Kidneys -- Imaging Computerized tomography Image segmentation Deep 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 42-45). Chronic kidney disease, often called chronic kidney failure, is a steady decline of renal function. Some of the most common reasons for kidney failure are cyst, stone and tumor. There may be no symptoms of chronic renal disease in its first stages. However, It’s possible to have kidney disease and not know it until it’s too late. Fortunately various neural networks have been shown to be beneficial in early disease prediction as machine learning and computer science has progressed. In this paper, we have used 5 CNN classification methods that are based on wa tershed segmentation and make use of deep neural networks (DNN) to classify 4 types (cyst,normal,stone,tumor) of kidney CT images. There are two stages to our work. We have first segmented the region of choice in CT images by watershed algo rithm. The segmented kidney data was then used to train a variety of classification networks, which includes EAnet and the transfer learning based pre-trained neu ral networks: ResNet50, VGG19, InceptionV3, and SqueezeNet. Our models were trained using the CT Kidney Normal Cyst Tumor and Stone dataset that was made public on Kaggle. Finally, EANet, SqueezeNet, VGG19, InceptionV3, and ResNet50 achieved 83.6%,97.3%,99.9%,98.8% and 87.9% of accuracy, respectively, on the test set of classification models. We observed that the modified VGG19 model had the highest sensitivity and specificity as well as the best overall accuracy. Mohammad Sakib Hossain S.M. Nazmul Hassan Md. Nakib rahaman Mohammad Al-Amin Rakib Hossain B. Computer Science 2023-05-30T04:08:56Z 2023-05-30T04:08:56Z 2022 2022-09 Thesis ID: 18341001 ID: 18301171 ID: 18301203 ID: 18301259 ID: 18301187 http://hdl.handle.net/10361/18371 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. 45 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Watershed Algorithm InceptionV3 Squeezenet ResNet50 VGG19 Transfer Learning EAnet Kidney diseases -- Diagnosis Kidneys -- Imaging Computerized tomography Image segmentation Deep learning |
| spellingShingle |
Watershed Algorithm InceptionV3 Squeezenet ResNet50 VGG19 Transfer Learning EAnet Kidney diseases -- Diagnosis Kidneys -- Imaging Computerized tomography Image segmentation Deep learning Hossain, Mohammad Sakib Hassan, S.M. Nazmul rahaman, Md. Nakib Al-Amin, Mohammad Hossain, Rakib Kidney Disease detection and classification from CT Images using Watershed Segmentation and 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 |
Hossain, Dr. Muhammad Iqbal |
| author_facet |
Hossain, Dr. Muhammad Iqbal Hossain, Mohammad Sakib Hassan, S.M. Nazmul rahaman, Md. Nakib Al-Amin, Mohammad Hossain, Rakib |
| format |
Thesis |
| author |
Hossain, Mohammad Sakib Hassan, S.M. Nazmul rahaman, Md. Nakib Al-Amin, Mohammad Hossain, Rakib |
| author_sort |
Hossain, Mohammad Sakib |
| title |
Kidney Disease detection and classification from CT Images using Watershed Segmentation and Deep Learning. |
| title_short |
Kidney Disease detection and classification from CT Images using Watershed Segmentation and Deep Learning. |
| title_full |
Kidney Disease detection and classification from CT Images using Watershed Segmentation and Deep Learning. |
| title_fullStr |
Kidney Disease detection and classification from CT Images using Watershed Segmentation and Deep Learning. |
| title_full_unstemmed |
Kidney Disease detection and classification from CT Images using Watershed Segmentation and Deep Learning. |
| title_sort |
kidney disease detection and classification from ct images using watershed segmentation and deep learning. |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/18371 |
| work_keys_str_mv |
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