Computed Tomography Kidney Disease Recognition using Deep Learning

Abstract

The present paper demonstrates kidney disease recognition in computed tomography (CT) images using deep neural networks, including convolutional neural networks (CNNs), the visual geometry group (VGG) network, and the deep residual network (ResNet-50). The kidney disease dataset consists of images among four categories, i.e cyst, normal, stone, and tumor. The class “cyst” consists of 3,709 images, the class “normal” consists of 5,077 images, the class “stone” consists of 1,377 images, and finally the class “tumor” consists of 2,283 images. In total, the dataset has 12,446 images. This dataset was passed through the deep CNN, VGG19, and ResNet-50 models to obtain the results. The major novelty of this work compared to previous works is that the algorithms of the CNN and ResNet-50 models have been modified, compared to existing works. The results, such as error/positive-predictive-value (PPV), error/probability of detection (POD), and error/area under the curve (AUC) curves, are shown for the confirmation of the work. Further, the error matrix, specificity/POD, and PPV/POD curves are also shown for the justification of the work. Finally, the performance metrics such as accuracy, PPV, POD, and F1-score values obtained from the error matrix are also shown for the justification of the work. The CNN model has achieved an accuracy of 0.95 for class “cyst”, 0.82 for class “normal”, 0.89 for class “stone”, and 0.87 for class “tumor”. The VGG19 neural network model has achieved an accuracy of 0.95 for class “cyst”, 0.79 for class “normal”, 0.84 for class “stone”, and 0.89 for class “tumor”. The ResNet-50 neural network model has achieved an accuracy of 0.92 for class “cyst”, 0.82 for class “normal”, 0.84 for class “stone”, and 0.92 for class “tumor”. The deep CNN model has achieved an area under the curve (AUC) value of 0.98 for class “cyst”, 0.87 for class “normal”, 0.92 for class “stone”, and 0.91 for class “tumor”. The deep CNN model has the highest AUC value for class “cyst” compared to other classes. The VGG19 model has an AUC value of 0.99 for class “cyst”, 0.92 for class “normal”, 1.00 for class “stone”, and 0.96 for class “tumor”. The VGG19 model has the highest AUC value for class “stone” compared to other classes. The ResNet-50 model has AUC values of 0.96 for class “cyst”, 0.92 for class “normal”, 0.92 for class “stone”, and 0.86 for class “tumor”. The ResNet-50 model has the highest AUC value for class “cyst” compared to the other classes.