YOLO-based thyroid ultrasound for assisted diagnosis

Abstract

Abstract Real-time intelligent diagnosis of thyroid ultrasound images using deep learning techniques can mitigate the impact of human subjective factors on diagnostic outcomes. Three thousand and fifty-five thyroid ultrasound images were acquired from 205 selected patients aged 10 to 77 years who underwent ultrasound examinations between 2019 and 2023. Each image contained at least one area of thyroid nodules, totaling 3, 088 nodules, including 1, 752 benign and 1, 336 malignant nodules. YOLOV8 is the baseline model for developing an end-to-end architecture for thyroid nodule detection. This architecture automatically identifies nodule lesions in ultrasound images, classifies them as benign or malignant, and enables real-time detection in video frames. The experimental results on 611 clinical thyroid ultrasound images demonstrate that our method can accurately diagnose benign and malignant nodal lesions in thyroid ultrasound with 88.1% and 84.6% accuracy, respectively. The mean average accuracy is as high as 91.1%, indicating a 5.1% improvement compared to the baseline model.