Title:Super-resolution based Nodule Localization in Thyroid Ultrasound Images
through Deep Learning
Volume: 20
Author(s): Jing Li, Qiang Guo*, Shiyi Peng and Xingli Tan
Affiliation:
- Department of Ultrasound, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, No. 1158 Gongyuan East Road, Qingpu District,
Shanghai, 201700, China
Keywords:
: Super-resolution, Thyroid nodule, Classification, Accuracy, Validation, Training, Nodule localization.
Abstract:
Background:
Currently, it is difficult to find a solution to the inverse inappropriate problem, which involves restoring a high-resolution image from a lowresolution
image contained within a single image. In nature photography, one can capture a wide variety of objects and textures, each with its own
characteristics, most notably the high-frequency component. These qualities can be distinguished from each other by looking at the pictures.
Objective:
The goal is to develop an automated approach to identify thyroid nodules on ultrasound images. The aim of this research is to accurately
differentiate thyroid nodules using Deep Learning Technique and to evaluate the effectiveness of different localization techniques.
Methods:
The method used in this research is to reconstruct a single super-resolution image based on segmentation and classification. The poor-quality
ultrasound image is divided into several parts, and the best applicable classification is chosen for each component. Pairs of high- and lowresolution
images belonging to the same class are found and used to figure out which image is high-resolution for each segment. Deep learning
technology, specifically the Adam classifier, is used to identify carcinoid tumors within thyroid nodules. Measures, such as localization accuracy,
sensitivity, specificity, dice loss, ROC, and area under the curve (AUC), are used to evaluate the effectiveness of the techniques.
Results:
The results of the proposed method are superior, both statistically and qualitatively, compared to other methods that are considered one of the latest
and best technologies. The developed automated approach shows promising results in accurately identifying thyroid nodules on ultrasound images.
Conclusion:
The research demonstrates the development of an automated approach to identify thyroid nodules within ultrasound images using super-resolution
single-image reconstruction and deep learning technology. The results indicate that the proposed method is superior to the latest and best
techniques in terms of accuracy and quality. This research contributes to the advancement of medical imaging and holds the potential to improve
the diagnosis and treatment of thyroid nodules.
