Kidney Tumor Segmentation and Classification using Deep Neural Network on CT Images
Md Humaion Kabir Mehedi, Ehteshamul Haque, Sameen Yasir Radin, Md Abrar Ur Rahman, Md Tanzim Reza, Md. Golam Robiul Alam
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 networks 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 classification method using Deep Neural Network (DNN). We have done our work in two Steps. Firstly, we have segmented kidneys using a manual segmentation technique and UNet along with SegNet for kidney segmentation. Then, for the classification 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 classification models MobileNetV2, VGG16, InceptionV3 scored with 95.29%, 99.48% and 97.38% accuracy on test set. We found that the VGG16 model has the best accuracy and the highest sensitivity and specificity. Explainable AI (GradCam) method has been applied to expalain our model's result.