Multi-scale ConvNeXt and late fusion of segmentation-derived morphological and echotextural features for the benign–malignant differentiation of thyroid nodules on ultrasound

This publication has 20 references indexed in Scilit:

• TN5000: An Ultrasound Image Dataset for Thyroid Nodule Detection and Classification
  Scientific Data, 2025
• UD-TN: A comprehensive ultrasound dataset for benign and malignant thyroid nodule classification
  Intelligent Oncology, 2025
• Shape-margin knowledge augmented network for thyroid nodule segmentation and diagnosis
  Computer Methods and Programs in Biomedicine, 2024
• Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data
  NeuroImage, 2023
• A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images
  Interdisciplinary Sciences: Computational Life Sciences, 2023
• Optimizing CNN based model for thyroid nodule classification using data augmentation, segmentation and boundary detection techniques
  Multimedia Tools and Applications, 2023
• Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules
  Computers in Biology and Medicine, 2022
• Classification of walnut varieties obtained from walnut leaf images by the recommended residual block based CNN model
  European Food Research and Technology, 2022
• Deep convolutional neural network for classification of thyroid nodules on ultrasound: Comparison of the diagnostic performance with that of radiologists
  European Journal of Radiology, 2022
• Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study
  The Lancet Digital Health, 2021