Detection of Thyroid Nodules with Ultrasound Images Based on Deep Learning

Abstract

Background: Thyroid nodules are a common clinical entity with high incidence. Ultrasound is often employed to detect and evaluate thyroid nodules. The development of an efficient automated method to detect thyroid nodules using ultrasound has the potential to reduce both physician workload and operator-dependence. Objective: To study the method of automatic detection of thyroid nodules based on deep learning using ultrasound, and to obtain the detection method with higher accuracy and better performance. Methods: A total of 1200 ultrasound images of thyroid nodules and 800 ultrasound thyroid images without nodule are collected. An improved faster R-CNN based detection method of thyroid nodule is proposed. Instead of using VGG16 as the backbone, ResNet is employed as the backbone for faster R-CNN. SVM, CNN and Faster-RCNN methods are used for thyroid nodule detection test. Precision, sensitivity, specificity and F1-score indicators are used to evaluate the detection performance of different methods. Results: The method based on deep learning is superior to that based on SVM. Faster R-CNN method and the improved method are better than CNN method. Compared with VGG16 as the backbone, RestNet101 backbone based faster R-CNN method achieves better thyroid detection effect. From the accuracy index, the proposed method is 0.084, 0.032 and 0.019 higher than SVM, CNN and faster R-CNN, respectively. Similar results can be seen in precision, sensitivity, specificity and F1-Score indicators. Conclusion: The proposed method of deep learning achieves the best performance values with the highest true positive and true negative detection compared to other methods and performs best in the detection of thyroid nodules.

Review of Deep Learning Approaches for Thyroid Cancer Diagnosis

“…erefore, a probable option to consider is to combine the specificity and accuracy of radiologists with the sensitivity of CAD systems and use these systems as assistants for operators with less experience at primary care centers [7,[10][11][12]. Accordingly, it is necessary to apply deep learning approaches and develop models with high accuracy, specificity, and sensitivity [68,69].…”

“…Ultrasonography is used to identify the characteristics of thyroid nodules as the primary diagnostic tool. ese identi ed characteristics help to classify nodules into benign or malignant type [10][11][12].…”

Discriminating the Nature of Thyroid Nodules Using the Hybrid Method

“…Mei et al extracted deep features of convolutional autoencoders and fundamental features including local binary patterns (LBP) as well as histogram of oriented gradients (HOG) descriptors in association with medical professional thyroid image characterization from B-US and trained the classifiers using these features to improve negative predictive value of thyroid nodule evaluation [15]. Comparison has been done between radiomics-based and deep learning-based approaches, and the results demonstrate that the deep learning-based method achieves a better performance [16,17]. Deep learning in conjunction with B-US image characterization could improve nodule characterization and reduce benign biopsies.…”

An image classification deep-learning algorithm for shrapnel detection from ultrasound images

“…Ultrasound image interpretation algorithms have been developed for a wide variety of applications, from tumor detection 9 , to thyroid nodules 10 , and lung pathologies due to COVID-19 11 . Image classification algorithms such as these primarily rely on supervised deep learning convolutional neural networks (CNN) to detect trends between positive and negative image sets.…”