Optimised RFO tuned RF-DETR model for precision urine microscopy for renal and systemic disease diagnosis - Neeraj Dahiya; Deo Prakash; Shakti Kundu; Shanu Rakesh Kuttan; Isha Suwalka; Manel Ayadi; Mitiku Dubale; Arshad Hashmi (Scientific Reports)
Abstract Accurate detection and classification of cellular and non-cellular components in urine microscopy images are essential for early diagnosis of renal and systemic health conditions. This study presents an optimized object detection framework based on the Red Fox Optimization (RFO)-enabled Roboflow-DEtection TRansformer (RF-DETR) model, designed to automate urine sediment analysis with high precision and low latency. The RF-DETR model leverages a transformer-based architecture with deformable attention and a DINOv2 (self-distillation with no labels) pre-trained visual backbone to capture multi-scale features effectively. RFO, a nature-inspired metaheuristic, is employed to fine-tune critical hyperparameters such as learning rate, decoder layers, and dropout, enhancing the model’s convergence and generalization capabilities. Experiments were conducted on the RF100-VL urine microscopy dataset, where the proposed model achieved a precision of 0.78, recall of 0.66, mAP@0.5 of 0.737, and mAP@0.5:0.95 of 0.44 after 100 training epochs. Compared to baseline models, the optimized RF-DETR demonstrated improved performance in detecting small and medium objects like leukocytes and erythrocytes—crucial components for urinary tract infection and kidney disease detection. The model’s NMS-free design and multi-resolution training enable real-time inference on both GPU and edge devices. Additionally, visualization tools such as confusion matrices, F1-curves, and prediction overlays validate the robustness and interpretability of the system. The results confirm the suitability of the RFO-optimized RF-DETR framework for clinical deployment, offering a powerful tool for automated, scalable, and accurate urine analysis. Future work will focus on lightweight model variants, enhanced small-object detection, and domain adaptation using self-supervised and vision-language learning techniques.