Thyroid nodule classification in ultrasound imaging using deep transfer learning.

Abstract

Background: The accurate diagnosis of thyroid nodules represents a critical and frequently encountered challenge in clinical practice, necessitating enhanced precision in diagnostic methodologies. In this study, we investigate the predictive efficacy of distinguishing between benign and malignant thyroid nodules by employing traditional machine learning algorithms and a deep transfer learning model, aiming to advance the diagnostic paradigm in this field.

Methods: In this retrospective study, ITK-Snap software was utilized for image preprocessing and feature extraction from thyroid nodules. Feature screening and dimensionality reduction were conducted using the least absolute shrinkage and selection operator (LASSO) regression method. To identify the optimal model, both traditional machine learning and transfer learning approaches were employed, followed by model fusion using post-fusion techniques. The performance of the model was rigorously evaluated through the area under the curve (AUC), calibration curve analysis, and decision curve analysis (DCA).

Results: A total of 1134 images from 630 cases of thyroid nodules were included in this study, comprising 589 benign nodules and 545 malignant nodules. Through comparative analysis, the support vector machine (SVM), which demonstrated the best diagnostic performance among traditional machine learning models, and the Inception V3 convolutional neural network model, based on transfer learning, were selected for model construction. The SVM model achieved an AUC of 0.748 (95% CI: 0.684-0.811) for diagnosing malignant thyroid nodules, while the Inception V3 transfer learning model yielded an AUC of 0.763 (95% CI: 0.702-0.825). Following model fusion, the AUC improved to 0.783 (95% CI: 0.724-0.841). The difference in performance between the fusion model and the traditional machine learning model was statistically significant (p = 0.036). Decision curve analysis (DCA) further confirmed that the fusion model exhibits superior clinical utility, highlighting its potential for practical application in thyroid nodule diagnosis.

Conclusion: Our findings demonstrate that the fusion model, which integrates a convolutional neural network (CNN) with traditional machine learning and deep transfer learning techniques, can effectively differentiate between benign and malignant thyroid nodules through the analysis of ultrasound images. This model fusion approach significantly optimizes and enhances diagnostic performance, offering a robust and intelligent tool for the clinical detection of thyroid diseases.