Comparison Study of Radiomics and Deep Learning-Based Methods for Thyroid Nodules Classification Using Ultrasound Images

Abstract

Thyroid nodules have a high prevalence and a small percentage is malignant. Many non-invasive methods have been developed with the help of the Internet of Things to improve the detection rate of malignant nodules. These methods can be roughly categorized into two classes: radiomics based and deep learning based approaches. In general, convolutional neural networks based deep learning methods have achieved promising performance in many medical image analysis and classification applications; however, no existing comparison has been done between radiomics based and deep learning based approaches. Therefore, in this paper, we aim to compare the performance of radiomics and deep learning based methods for the classification of thyroid nodules from ultrasound images. On one hand, we developed a radiomics based method, which consists of extracting high throughput 302-dimensional statistical features from pre-processed images. Then dimension reduction was performed using mutual information and linear discriminant analysis respectively to achieve the final classification. On the other hand, a deep learning based method was also developed and tested by pre-training a VGG16 model with fine-tuning. Ultrasound images including 3120 images (1841 benign nodules and 1393 malignant nodules) from 1040 cases were retrospectively collected. The dataset was divided into 80% training and 20% testing data. The highest accuracies yielded on the testing data for radiomics and deep learning based methods were 66.81% and 74.69%, respectively. A comparison result demonstrated that the deep learning based method can achieve a better performance than using radiomics.