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Performance study of lightweight YOLO-SAM model for non-standard superficial tumor image detection

algorithms-and-analysis-of-algorithms
artificial-intelligence
computer-vision
data-mining-and-machine-learning
neural-networks

Abstract

The early detection of superficial tumors is an important research object in plastic surgery and dermatology, which is crucial for improving patient prognosis. In this study, we propose an intelligent diagnostic framework based on non-standardized superficial tumor images by YOLO series of target detection models (v7 to v10) and the segmentation capability of the segment anything model (SAM) to achieve efficient screening and boundary depiction for ten types of common superficial tumors. The dataset was derived from non-standard clinical images from the Department of Plastic and Reconstructive Surgery of the Affiliated Hospital of Qingdao University, covering diverse lighting, equipment, and background conditions to simulate real-life scenarios. Experimental results indicated that YOLOv10n performs the best detection (F1-score 0.912, [email protected] 0.912, total inference time 4.3 ms). Additionally, YOLOv8n surpasses conventional models, including Faster R-CNN and EfficientDet, with exceptional accuracy (0.952). Despite the uneven distribution of the data and the image variability, which present challenges for rare category (blue mole) detection, the hybrid YOLO-SAM framework demonstrates robustness and real-time performance. This study provides technical support for automated superficial tumor detection in non-standardized scenarios . I ts lightweight design is suitable for low-cost devices , including smartphones, which can promote remote screening applications and improve the efficiency of early diagnosis of superficial tumors and patient prognosis.
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