A Reliable Kidney Stone Detection Method Using Inductive Transfer-Based Ensemble Deep Neural Networks - Muthabathula Manoj Rajiv; B.S.N. Murthy (The Bioscan, 2025)
Chronic kidney disease is a significant global health concern, with kidney stones being a major contributing factor to kidney dysfunction. Early and accurate detection is essential to prevent severe complications. This study proposes an efficient approach using inductive transfer-based ensemble deep neural networks for kidney stone detection. A combination of classification models, including DarkNet19, InceptionV3, ResNet101, and others, along with detection algorithms from the YOLO family, enhances diagnostic accuracy. Feature extraction techniques such as ReliefF and validation methods like KNN and KFold improve model performance. The integration of the Xception model further refines classification accuracy, while a user-friendly Flask-based front end facilitates real-time testing with secure authentication. The proposed approach improves early diagnosis, reduces physician workload, and enhances patient care.