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arXiv · 2501.16469

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model

Weijie He·Yuwei Zhang·Ting Xu·Tai An·Yingbin Liang·Bo Zhang
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Abstract

Deep learning has emerged as a transformative approach for solving complex pattern recognition and object detection challenges. This paper focuses on the application of a novel detection framework based on the RT-DETR model for analyzing intricate image data, particularly in areas such as diabetic retinopathy detection. Diabetic retinopathy, a leading cause of vision loss globally, requires accurate and efficient image analysis to identify early-stage lesions. The proposed RT-DETR model, built on a Transformer-based architecture, excels at processing high-dimensional and complex visual data with enhanced robustness and accuracy. Comparative evaluations with models such as YOLOv5, YOLOv8, SSD, and DETR demonstrate that RT-DETR achieves superior performance across precision, recall, mAP50, and mAP50-95 metrics, particularly in detecting small-scale objects and densely packed targets. This study underscores the potential of Transformer-based models like RT-DETR for advancing object detection tasks, offering promising applications in medical imaging and beyond.

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Weijie He, Yuwei Zhang, Ting Xu, Tai An, Yingbin Liang, Bo Zhang. 2025-01-27. Object Detection for Medical Image Analysis: Insights from the RT-DETR Model. https://arxiv.org/abs/2501.16469

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