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

• URL: http://arxiv.org/abs/2501.16469v1
• Date: Mon, 27 Jan 2025 20:02:53 GMT
• Title: Object Detection for Medical Image Analysis: Insights from the RT-DETR Model
• Authors: Weijie He, Yuwei Zhang, Ting Xu, Tai An, Yingbin Liang, Bo Zhang,
• Abstract summary: This paper focuses on the application of a novel detection framework based on the RT-DETR model for analyzing intricate image data. 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.
• Score: 40.593685087097995
• License:
• 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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