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Deep Learning Routes to Thyroid Ultrasound Image Segmentation; a Review

EasyChair Preprint no. 11296

27 pagesDate: November 15, 2023


On the forward-facing of the neck, the thyroid gland yields hormones which support in regulating the digestion. Thyroid problems are most typically detected and classified via ultrasound (US) imaging. US imaging has become one of the most important contributions for analyzing thyroid disorders due to its safety, accessibility, non-invasiveness and cost-effectiveness. Machine learning (ML) advances, especially deep learning (DL) are proving to be beneficial in recognising and quantifying patterns in clinical images. At the heart of these advancements is DL algorithms' ability to extract hierarchical feature representations directly from images, eliminating the requirement for constructed features. This study describes the evolution of ML, the concepts of DL algorithms, and an overview of successful applications, including clinical picture segmentation for US imaging of thyroid-related illnesses. Finally, certain research difficulties are mentioned along with future enhancements.

Keyphrases: deep learning, image segmentation, Thyroid, Ultrasound

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Jatinder Kumar and Surya Panda and Devi Dayal},
  title = {Deep Learning Routes to Thyroid Ultrasound Image Segmentation; a Review},
  howpublished = {EasyChair Preprint no. 11296},

  year = {EasyChair, 2023}}
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