ThyroNet-X4 Genesis: An Advanced Deep Learning Model for Auxiliary Diagnosis of Thyroid Nodules' Malignancy

Abstract

Thyroid nodules are common endocrine disorders, and the discrimination between benign and malignant nodules is crucial for treatment decisions. Traditional ultrasound diagnosis relies on the experience of physicians, which may pose risks of misdiagnosis. In this study, we propose a novel deep learning model, ThyroNet-X4 Genesis, for the automatic classification of thyroid nodules' malignancy. This model is based on the ResNet module, which optimizes computational efficiency and enhances feature extraction capabilities by introducing grouped convolution and increasing the convolution kernel size, thus extracting features and classifying nodules in ultrasound images. We obtained data from publicly available medical imaging databases for internal training and validation and used ultrasound images collected from HanZhong Central Hospital as an external validation set to evaluate the model's generalization ability and practical application value.ThyroNet-X4 Genesis achieved training and validation accuracies of 85.55% and 71.70%, respectively, on the internal validation set, with a testing accuracy of 67.02% on the external validation set, outperforming other mainstream comparative models, indicating its good performance in actual clinical applications. The development of this model showcases the potential of deep learning in thyroid imaging analysis, providing valuable references for future development of high-performance medical diagnostic models.