AI-Renal Care: Transfer Learning and Ensemble Methods for Accurate Renal Cancer Diagnosis

Renal cancer diagnosis has become a critical area of research in medical imaging, with the advent of artificial intelligence and machine learning providing significant advancements in accuracy and efficiency. The ability to detect and classify kidney tumors at an early stage plays a crucial role in improving patient outcomes and treatment planning. However, challenges such as limited labeled data, variation in imaging modalities, and the complexity of tumor detection continue to hinder the development of effective diagnostic systems. This research addresses these issues by utilizing advanced techniques like transfer learning and ensemble learning to enhance the accuracy of renal cancer diagnosis. By applying DenseNet for feature extraction and XGBoost for classification, the study aims to improve detection performance across multiple imaging modalities, including CT, MRI, and PET scans. Additionally, the use of K-fold cross-validation ensures robustness and generalization for real-world clinical applications. The novelty of this work lies in the integration of transfer learning for deep feature extraction, combined with ensemble techniques such as XGBoost to enhance model performance. Unlike previous approaches, this methodology optimizes the model for better accuracy and generalization while addressing data variability and class imbalance in medical imaging. The contribution of this research lies in demonstrating the feasibility and efficiency of using AI-driven techniques for renal cancer diagnosis, offering a scalable solution for real-time healthcare applications.