Segmentation based Kidney Tumor Classification using Deep Neural Network
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
2022
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| Länkar: | http://hdl.handle.net/10361/17120 |
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10361-171202022-08-24T21:01:38Z Segmentation based Kidney Tumor Classification using Deep Neural Network Mehedi, Md Humaion Kabir Haque, Ehteshamul Radin, Sameen Yasir Ur Rahman, Md. Abrar Alam, Dr. Md. Golam Rabiul Reza, Md Tanzim Department of Computer Science and Engineering, Brac University Kidney Tumor Computed Tomography (CT) VGG16 Segmentation Classification Deep Neural Network (DNN) Neural network Neural networks (Computer science) 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 37-40). Kidney disease is one of many severe chronic disease that a person can have. Early detection of this disease can be pivotal for proper treatment. Different neural net works have proven to be useful in disease prediction in the progression of modern science. In this paper, we have proposed a segmentation based kidney tumor clas sification using Deep Neural Network (DNN). We have done our work in two Steps. Firstly, we have segmented kidneys using a manual segmentation technique and trained UNet along with SegNet for kidney segmentation. Then, for the classifica tion task, the modified MobileNetV2, VGG16 and InceptionV3 was trained on the segmented kidney data. CT KIDNEY DATASET: Normal-Cyst-Tumor and Stone dataset(published in Kaggle) was used to train our models. Finally, the classifica tion models MobileNetV2, VGG16, InceptionV3 scored with 95.29%, 99.21% and 97.38% accuracy on test set. We found that the modified VGG16 model has the best accuracy and the highest sensitivity and specificity. Md Humaion Kabir Mehedi Ehteshamul Haque Sameen Yasir Radin Md. Abrar Ur Rahman B. Computer Science 2022-08-24T09:15:19Z 2022-08-24T09:15:19Z 2022 2022-01 Thesis ID: 17201061 ID: 18101481 ID: 17221003 ID: 18101276 http://hdl.handle.net/10361/17120 en_US 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. 40 Pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Kidney Tumor Computed Tomography (CT) VGG16 Segmentation Classification Deep Neural Network (DNN) Neural network Neural networks (Computer science) |
| spellingShingle |
Kidney Tumor Computed Tomography (CT) VGG16 Segmentation Classification Deep Neural Network (DNN) Neural network Neural networks (Computer science) Mehedi, Md Humaion Kabir Haque, Ehteshamul Radin, Sameen Yasir Ur Rahman, Md. Abrar Segmentation based Kidney Tumor Classification using Deep Neural Network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022 |
| author2 |
Alam, Dr. Md. Golam Rabiul |
| author_facet |
Alam, Dr. Md. Golam Rabiul Mehedi, Md Humaion Kabir Haque, Ehteshamul Radin, Sameen Yasir Ur Rahman, Md. Abrar |
| format |
Thesis |
| author |
Mehedi, Md Humaion Kabir Haque, Ehteshamul Radin, Sameen Yasir Ur Rahman, Md. Abrar |
| author_sort |
Mehedi, Md Humaion Kabir |
| title |
Segmentation based Kidney Tumor Classification using Deep Neural Network |
| title_short |
Segmentation based Kidney Tumor Classification using Deep Neural Network |
| title_full |
Segmentation based Kidney Tumor Classification using Deep Neural Network |
| title_fullStr |
Segmentation based Kidney Tumor Classification using Deep Neural Network |
| title_full_unstemmed |
Segmentation based Kidney Tumor Classification using Deep Neural Network |
| title_sort |
segmentation based kidney tumor classification using deep neural network |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17120 |
| work_keys_str_mv |
AT mehedimdhumaionkabir segmentationbasedkidneytumorclassificationusingdeepneuralnetwork AT haqueehteshamul segmentationbasedkidneytumorclassificationusingdeepneuralnetwork AT radinsameenyasir segmentationbasedkidneytumorclassificationusingdeepneuralnetwork AT urrahmanmdabrar segmentationbasedkidneytumorclassificationusingdeepneuralnetwork |
| _version_ |
1814306999306813440 |