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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background:

Currently, it is difficult to find a solution to the inverse inappropriate problem, which involves restoring a high-resolution image from a low-resolution 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 low-resolution 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.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-05-17
2025-06-24

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/content/journals/cmir/10.2174/0115734056269264240408080443
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Super-resolution based Nodule Localization in Thyroid Ultrasound Images through Deep Learning
Curr. Med. Imaging 20, e15734056269264 (2024); https://doi.org/10.2174/0115734056269264240408080443
/content/journals/cmir/10.2174/0115734056269264240408080443
/content/journals/cmir/10.2174/0115734056269264240408080443
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  • Article Type:     Research Article
Keyword(s): Accuracy; Classification; Nodule localization; Super-resolution; Thyroid nodule; Training; Validation

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