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Ultrasound Nerve Segmentation and Injury Detection Using Deep Learning

  • 2026
  • OriginalPaper
  • Chapter
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Abstract

This chapter explores the use of deep learning techniques, specifically convolutional neural networks (CNNs) like U-Net, for ultrasound nerve segmentation and injury detection. The text begins with an introduction to the importance of nerve segmentation and injury detection in medical imaging, highlighting the limitations of traditional methods. A literature review discusses various studies that have applied U-Net and other deep learning models to ultrasound nerve segmentation, emphasizing the accuracy and efficiency of these approaches. The proposed methodology involves data preprocessing, model training, and evaluation, with a focus on achieving precise nerve segmentation and damage detection. The results demonstrate the effectiveness of the proposed method in identifying nerve structures and detecting injuries, with potential applications in early diagnosis and improved patient care. The chapter concludes by discussing the broader implications of these findings and the potential for future research in this field.

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Title
Ultrasound Nerve Segmentation and Injury Detection Using Deep Learning
Authors
Parige Deepthi Priya
Kagitha Karthik
Vinjamuri Satya Sri Madhurya
Nakshatra Sriramoju
P. Chandini
J. Adilakshmi
Copyright Year
2026
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-95-0269-1_62
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