An optimized fusion of deep learning models for kidney stone detection from CT images

Accurate diagnosis of kidney disease is crucial, as it is a significant health concern that demands precise identification for effective and appropriate treatment. Deep learning methods are increasingly recognized as valuable tools for disease diagnosis in the biomedical field. However, current models utilizing deep networks often encounter challenges of overfitting and low accuracy, necessitating further refinement for optimal performance. To overcome these challenges, this paper proposes the introduction of two ensemble models designed for kidney stone detection in CT images. The first model, called StackedEnsembleNet, is a two-level deep stack ensemble model that effectively integrates the predictions from four base models: InceptionV3, InceptionResNetV2, MobileNet, and Xception. By leveraging the collective knowledge of these models, StackedEnsembleNet improves the accuracy and reliability of kidney stone detection. The second model PSOWeightedAvgNet, leverages the Particle Swarm Optimization (PSO) algorithm to determine the optimal weights for the weighted average ensemble. Through PSO, this ensemble approach assigns optimized weights to each model during the ensembling process, effectively enhancing the performance by optimizing the combination of their predictions. Experimental results conducted on a large dataset of 1799 CT images demonstrate that both StackedEnsembleNet and PSOWeightedAvgNet outperform the individual base models, achieving high accuracy rates in kidney stone detection. Furthermore, additional experiments on an unseen dataset validate the models' ability to generalize. The comparison with previous methods confirms the superior performance of the proposed ensemble models. The paper also presents Grad-CAM visualizations and error case analysis to provide insights into the decision-making processes of the models. By overcoming the limitations of existing deep learning models, StackedEnsembleNet and PSOWeightedAvgNet offer a promising approach for accurate kidney stone detection, contributing to improved diagnosis and treatment outcomes in the field of nephrology.