Deep Learning and Optimization Approaches in Segmentation and Classification of Renal Tumors Using EfficientNet-Based U-Net and Epistemic Neural Networks: A Review

Abstract

Renal tumor detection and segmentation are critical tasks in medical imaging, significantly influencing early diagnosis, treatment planning, and patient survival. Traditional manual segmentation methods are time-consuming and prone to inter-observer variability, necessitating automated solutions. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have demonstrated remarkable performance in medical image segmentation. Among these, U-Net and its variants have become the dominant architectures due to their ability to learn from limited datasets and produce precise segmentation outputs. Furthermore, the integration of EfficientNet as an encoder backbone enhances feature extraction efficiency and model scalability, leading to improved segmentation accuracy.

In addition, epistemic neural networks introduce uncertainty quantification, addressing the reliability issues of deep learning models in clinical settings. These models help identify uncertain predictions, thereby improving trustworthiness in automated diagnosis. This review provides a comprehensive analysis of deep learning techniques for renal tumor segmentation and classification, focusing on EfficientNet-based U-Net architectures and epistemic learning approaches. A comparative study of recent literature is presented, highlighting performance metrics, datasets, and limitations. The study concludes by identifying research gaps and future directions toward developing robust, explainable, and clinically reliable AI systems for renal tumor analysis.