An Efficient and Robust Approach Using Inductive Transfer-Based Ensemble Deep Neural Networks for Kidney Stone Detection

Abstract

Chronic kidney disorder is a global health problem involving the repercussions of impaired kidney function and kidney failure. A kidney stone is a kidney scenario that impairs kidney function. Because this disease is usually asymptomatic, early and quick detection of kidney problems is essential to avoid significant consequences. This study presents an automated detection of Computed Tomography (CT) kidney stone images using an inductive transfer-based ensemble Deep Neural Network (DNN). Three datasets are created for feature extraction from kidney CT images using pre-trained DNN models. After assembling several pre-trained DNNs, such as DarkNet19, InceptionV3, and ResNet101, the ensemble deep feature vector is created using feature concatenation. The Iterative ReliefF feature selection method is used to choose the most informative ensemble deep feature vectors, which are then fed into the K Nearest Neighbor classifier tuned using a Bayesian optimizer with a 10-fold cross-validation approach to detect kidney stones. The proposed strategy achieves 99.8% and 96.7% accuracy using the quality and noisy image datasets, which are superior to other DNN-based and traditional image detection approaches. This proposed automated approach can help urologists confirm their physical inspection of kidney stones, reducing the possibility of human mistakes.