Kidney Disease Detection from CT Images using a customized CNN model and Deep Learning

Chronic kidney disease, often called chronic kidney failure, is a steady decline of renal function. Some of the most common reasons for kidney failure are cysts, stones, and tumors. There may be no symptoms of chronic renal disease in its first stages. However, It’s possible to have kidney disease and not know it until it’s too late. Fortunately, various neural networks have been shown to be beneficial in early disease prediction as machine learning and computer science have progressed. In this paper, we have used 3 CNN classification methods that are based on watershed segmentation and make use of deep neural networks (DNN) to classify 4 types (cyst, normal, stone, tumor) of kidney CT images. There are two stages to our work. We have first segmented the region of choice in CT images by the watershed algorithm. The segmented kidney data was then used to train a variety of classification networks, which includes EAnet and the transfer learning-based pre-trained neural network: ResNet50, and a customized CNN model. The models were trained using the CT Kidney Normal Cyst Tumor and Stone dataset that was made public on Kaggle. Finally, EANet, ResNet50, and the proposed CNN model achieved 83.65%, 87.92%, and 98.66% of accuracy, respectively, on the test set of classification models. We observed that the proposed CNN model had the highest sensitivity and specificity as well as the best overall accuracy.