A Comprehensive Multi-Model Approach to Kidney Stone Detection using Deep Learning

Abstract

This work presents an end-to-end deep learning-based kidney stone detection system based on a combination of Convolutional Neural Networks (CNNs) for ultrasound image processing and Random Forest for clinical data classification. The combination of the two models has an enormously improved diagnostic accuracy, reducing misclassification rates. With the use of a fusion model, the system effectively facilitates decision-making with accurate and autonomous detection. This study illustrates the advantages of multi-modal learning in medical diagnosis and aims to support healthcare professionals in early detection and treatment planning.

Introduction

Early identification of kidney stones is crucial to reduce pain and complications. Traditional diagnosis relies on ultrasound, blood, and urine tests interpreted subjectively by radiologists, which can be time-consuming and error-prone. Advances in machine learning and deep learning, particularly Convolutional Neural Networks (CNNs), have improved medical image analysis by automatically extracting features and classifying ultrasound images. Meanwhile, Random Forest models effectively analyze structured clinical data like urine composition and blood markers.

However, single-modality approaches face limitations: CNNs struggle with poor-quality images or artifacts, while clinical data alone lack spatial context. To overcome these issues, this project proposes a multi-modal system combining CNN-based image analysis and Random Forest clinical data analysis. The fusion model weights predictions from both to enhance accuracy and reduce false positives/negatives.

The methodology includes preprocessing ultrasound images and clinical data, training separate models, and merging their outputs for final diagnosis. The system is developed using Python with libraries like TensorFlow, Keras, and Scikit-learn. Performance is evaluated by accuracy, precision, recall, and F1-score.

Results show that integrating deep learning with clinical data significantly improves kidney stone detection, accurately distinguishing stones from tumors, cysts, and normal cases, supporting better, faster clinical decisions.

Conclusion

The Kidney Stone Detection System elegantly combines deep learning-based image classification with machine learning-based clinical data analysis to enable kidney stone diagnosis with enhanced accuracy and efficiency. Utilizing the application of Convolutional Neural Networks (CNNs) for image processing of ultrasound images and Random Forest models for clinical data analysis, the system presents an end-to-end and data-driven solution to medical diagnostics. The combination of the two models leads to a more precise prediction, ruling out false positives and false negatives, which are rampant with normal diagnosis techniques. This model can provide a mechanistic and scalable system with less human interpretation dependency, enabling standardized and faster diagnosis. Real-time reporting and monitoring also facilitate the health care experts to make well-informed decisions. The strength of this model is in the weighted approach of image-based and clinical predictions given by the fusion model and then weighing them in turn to receive an overall indication of kidney wellness. Future expansion for this system consists of bigger data sets, hyperparameter adjustment of the model, and inclusion of more medical features like genetic predisposition and lifestyle. Future work in explainable AI will enable greater transparency, so that doctors can have more understanding of why they arrived at their decision. With ongoing enhancement, this system has strong potential in maximizing early detection, minimizing misdiagnosis, and enhancing patient care in nephrology.

References

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Copyright

Copyright © 2025 Ramagowni SivaKumar, Hrushikesh Reddy K V, Charan Reddy S, Surya Prakash Reddy B. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.