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Related Experiment Video

Updated: Jun 4, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Tensor enhanced chest cancer classification via CNN and Vision Transformer models.

Nayab Asim1, Mehreen Sirshar1, Mohammad Zubair Khan2

  • 1Software Engineering, Fatima Jinnah Women University, Rawalpindi, Punjab, Pakistan.

Plos One
|June 2, 2026
PubMed
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This study compares deep learning models for lung cancer detection using CT/PET-CT scans. ResNet-50 and EfficientNet showed high accuracy, while Vision Transformer captured complex patterns, highlighting AI

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Oncology
  • Deep Learning for Disease Detection

Background:

  • Lung cancer is a major global health concern, necessitating improved diagnostic tools.
  • Early and accurate diagnosis of lung cancer is crucial for effective patient treatment and survival rates.
  • Current diagnostic methods can be enhanced by advanced computational approaches.

Purpose of the Study:

  • To evaluate and compare various convolutional neural network (CNN) architectures against a Vision Transformer (ViT) model for lung cancer classification.
  • To introduce a unified platform for preprocessing medical images using a tensor-based pipeline for deep learning models.
  • To assess the performance of different deep learning models on the YOLOTransfer dataset for CT/PET-CT image classification.

Main Methods:

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Last Updated: Jun 4, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

  • A common tensor-based preprocessing pipeline was applied to all input CT/PET-CT images.
  • Convolutional neural network models (AlexNet, VGG-16, ResNet-50, DenseNet, EfficientNet) were compared with a Vision Transformer (ViT) model.
  • Model performance was evaluated using metrics including accuracy, sensitivity, specificity, F1-score, and AUC-ROC on the YOLOTransfer dataset.

Main Results:

  • ResNet-50 and EfficientNet demonstrated the highest accuracy in lung cancer classification.
  • The Vision Transformer model exhibited competitive performance, particularly in identifying complex global patterns within the images.
  • The tensor-based preprocessing pipeline facilitated implicit fine-tuning across different model architectures.

Conclusions:

  • Deep learning models, including CNNs and ViTs, show significant potential for accurate lung cancer detection from CT/PET-CT imaging.
  • The study highlights the complementary strengths of convolutional and transformer-based architectures in medical image analysis.
  • The findings support the feasibility and effectiveness of AI-driven approaches for improving early lung cancer diagnosis.