Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: A comprehensive study.

Journal: Computer methods and programs in biomedicine
PMID: 38723436

Abstract

BACKGROUND AND OBJECTIVE: The thyroid gland, a key component of the endocrine system, is pivotal in regulating bodily functions. Thermography, a non-invasive imaging technique utilizing infrared cameras, has emerged as a diagnostic tool for thyroid-related conditions, offering advantages such as early detection and risk stratification. Artificial intelligence (AI) has demonstrated success in medical diagnostics, and its integration into thermal imaging analysis holds promise for improving diagnostic capabilities. This study aims to explore the potential of AI, specifically convolutional neural networks (CNNs), in enhancing the analysis of thyroid thermograms for the detection of nodules and abnormalities.

Authors

  • Mehdi Etehadtavakol
    School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, 1985717443, Iran; Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Mahnaz Etehadtavakol
    Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81745-33871, Iran.
  • Eddie Y K Ng
    School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.

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