Identification of lesion location and discrimination between benign and malignant findings in thyroid ultrasound imaging
Abstract 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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-83888-1 |
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| Summary: | Abstract 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. |
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| ISSN: | 2045-2322 |