Ensemble Deep Learning for Enhanced CT-Scan Kidney Stone Classification

Abstract

Accurate classification of kidney conditions from CT-scan images is critical for timely diagnosis and treatment. This study presents an ensemble deep learning approach leveraging fine-tuned VGG-16 architecture for enhanced classification of kidney CT scans into four distinct categories: cyst, normal, stone, and tumor. The proposed model integrates transfer learning and layer-wise fine-tuning of the VGG-16 network, optimizing the final 20 layers to extract high-level, domain-specific features. Ensemble techniques are employed to boost model robustness and minimize variance, combining predictions from multiple fine-tuned instances to achieve superior generalization. A dataset of annotated CT-scan images was preprocessed using normalization and augmentation strategies to improve model performance and prevent overfitting. Experimental evaluation demonstrates a classification accuracy of 96%, significantly outperforming traditional single-model baselines. The model’s effectiveness in distinguishing among clinically relevant kidney conditions makes it a valuable tool for computer-aided diagnosis (CAD) systems. Additionally, the model exhibits strong consistency across all four classes, with minimal confusion in challenging cases such as differentiating cysts from tumors. The promising results highlight the potential of fine-tuned ensemble CNNs in medical image classification and underscore the importance of model interpretability and validation in clinical applications. Future work includes real-time deployment and integration with radiology workflows.