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

Bibliografiska uppgifter
Huvudupphovsmän: Mehedi, Md Humaion Kabir, Haque, Ehteshamul, Radin, Sameen Yasir, Ur Rahman, Md. Abrar
Övriga upphovsmän: Alam, Dr. Md. Golam Rabiul
Materialtyp: Lärdomsprov
Språk:en_US
Publicerad: Brac University 2022
Ämnen:
Länkar:http://hdl.handle.net/10361/17120
id 10361-17120
record_format dspace
spelling 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
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