Identification of lesion location and discrimination between benign and malignant findings in thyroid ultrasound imaging.

Thyroid nodules are a common thyroid disorder, and ultrasound imaging, as the primary diagnostic tool, is susceptible to variations based on the physician's experience, leading to misdiagnosis. This paper constructs an end-to-end thyroid nodule detection framework based on YOLOv8, enabling automatic detection and classification of nodules by extracting grayscale and elastic features from ultrasound images. First, an attention-weighted DCN is introduced to enhance superficial feature extraction and capture local information. Next, the CPCA mechanism is employed to reduce the interference of redundant information. Finally, a feature fusion network based on an aggregation-distribution mechanism is utilized to improve the learning capability of fine-grained features, enhancing the performance of early nodule detection. Experimental results demonstrate that our method is accurate and effective for thyroid nodule detection, achieving diagnostic rates of 89.3% for benign and 90.4% for malignant nodules based on tests conducted on 611 clinical ultrasound images, with a mean Average Precision at IoU = 0.5 (mAP@50) of 95.5%, representing a 6.6% improvement over baseline models.