Enhancing thyroid nodule detection in ultrasound images : a novel YOLOv8 architecture with a C2fA module and optimized loss functions

Abstract

Thyroid-related diseases, particularly thyroid cancer, are rising globally, emphasizing the critical need for the early detection and accurate screening of thyroid nodules. Ultrasound imaging has inherent limitations—high noise, low contrast, and blurred boundaries—that make manual interpretation subjective and error-prone. To address these challenges, YOLO-Thyroid, an improved model for the automatic detection of thyroid nodules in ultrasound images, is presented herein. Building upon the YOLOv8 architecture, YOLO-Thyroid introduces the C2fA module—an extension of C2f that incorporates Coordinate Attention (CA)—to enhance feature extraction. Additionally, loss functions were incorporated, including class-weighted binary cross-entropy to alleviate class imbalance and SCYLLA-IoU (SIoU) to improve localization accuracy during boundary regression. A publicly available thyroid ultrasound image dataset was optimized using format conversion and data augmentation. The experimental results demonstrate that YOLO-Thyroid outperforms mainstream object detection models across multiple metrics, achieving a higher detection precision of 54%. The recall, calculated based on the detection of nodules containing at least one feature suspected of being malignant, reaches 58.2%, while the model maintains a lightweight structure. The proposed method significantly advances ultrasound nodule detection, providing an effective and practical solution for enhancing diagnostic accuracy in medical imaging.