Alam, Dr. Md. Golam RabiulReza, Md TanzimMehedi, Md Humaion KabirHaque, EhteshamulRadin, Sameen YasirUr Rahman, Md. Abrar2022-08-242022-08-2420222022-01ID: 17201061ID: 18101481ID: 17221003ID: 18101276http://hdl.handle.net/10361/17120Cataloged from PDF version of thesis.Includes bibliographical references (pages 37-40).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022Kidney 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.40 Pagesen-USBrac 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.Kidney TumorComputed Tomography (CT)VGG16SegmentationClassificationDeep Neural Network (DNN)Neural networkNeural networks (Computer science)Thesis