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

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

Published on: April 21, 2023

Trans RCED-UNet3+: a hybrid CNN-transformer model for precise lung nodule segmentation.

Sadaf Raza¹, Razia Zia^1,2, Irfan Ahmed Usmani³

¹Department of Electronic Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan.

Frontiers in Oncology

|November 3, 2025

Summary

This summary is machine-generated.

This study introduces Trans RCED-UNet3+ for precise lung nodule segmentation in CT scans. The enhanced model improves accuracy for better lung cancer diagnosis.

Keywords:

LIDC-IDRI RCED-UNet 3+hybrid loss function lung nodule transformer bottleneck

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate lung nodule segmentation in CT scans is critical for lung cancer diagnosis.
Challenges include small nodule size and complex shapes, hindering precise segmentation.

Purpose of the Study:

To present Trans RCED-UNet3+, an advanced framework for enhanced lung nodule segmentation.
To improve the accuracy and efficiency of detecting lung nodules in medical images.

Main Methods:

Utilized a transformer-based bottleneck for global context and long-range dependency capture.
Incorporated residual connections for efficient feature flow and gradient preservation.
Employed a hybrid Dice and Binary Cross-Entropy loss function to refine nodule boundary segmentation.

Main Results:

Trans RCED-UNet3+ achieved a Dice score of 0.990 on the LIDC-IDRI dataset.
This represents an improvement over the original RCED-UNet3+ model's score of 0.984.

Conclusions:

The integration of convolutional and transformer architectures offers a robust approach for medical image segmentation.
The proposed model enhances the detection of subtle and irregular lung nodule structures, supporting more accurate clinical diagnoses.