YOLOv8 vs. Mask R-CNN: A Comparative Analysis of for Glomeruli Instance Segmentation in Kidney Tissues

Wildfires Accurate and timely analysis of glomeruli is paramount in deciphering kidney diseases and guiding treatment strategies. However, traditional manual segmentation methods are laborious, susceptible to inter-observer variability, and impede real-time diagnosis. This research delves into the realm of deep learning, investigating the efficacy of two prominent models, YOLOv8 and Mask R-CNN, for automated glomeruli instance segmentation in kidney images. Our comprehensive study meticulously compares the performance of both models, meticulously evaluating metrics like precision, recall, and mAP50. The results paint a compelling picture, with YOLOv8 emerging as the champion. It exhibits superior precision (0.97 versus 0.85 for Mask R-CNN), recall (0.85 versus 0.78), and mAP50 (0.93 versus 0.85), demonstrating its prowess in accurately delineating glomeruli. Furthermore, YOLOv8 shines in terms of computational efficiency, boasting significantly faster inference times (3.3 ms versus 7.5 ms for Mask R-CNN). This lightning-fast performance renders it exceptionally suitable for real-time applications, enabling rapid diagnoses and prompt interventions in critical situations. The findings of this investigation unveil YOLOv8's immense potential as a robust and efficient tool for automated glomeruli segmentation. Its remarkable accuracy, coupled with real-time processing capabilities, paves the way for a paradigm shift in kidney disease diagnostics.