Segmentation based Kidney Tumor Classification using Deep Neural Network

Abstract

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.