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Related Experiment Video

Updated: Aug 19, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation.

P Malin Bruntha¹, S Immanuel Alex Pandian¹, K Martin Sagayam¹

¹Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.

Scientific Reports

|November 26, 2022

Summary

This summary is machine-generated.

Lung nodule segmentation in CT scans is crucial for early lung cancer detection. A new Lung_PAYNet model significantly improves segmentation accuracy, achieving high performance metrics.

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

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Last Updated: Aug 19, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

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Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Accurate lung nodule segmentation in computed tomography (CT) is vital for early lung cancer diagnosis.
Challenges include diverse nodule appearances, proximity to other structures, and visual similarities.

Purpose of the Study:

To introduce Lung_PAYNet, a novel pyramidal attention-based architecture for enhanced lung nodule segmentation in low-dose CT images.
To evaluate the performance of Lung_PAYNet against existing methods like UNet.

Main Methods:

Developed Lung_PAYNet, featuring an encoder-decoder structure with inverted residual blocks and swish activation.
Integrated a feature pyramid attention network for precise dense feature extraction.
Trained and validated the model using the public LIDC-IDRI dataset.

Main Results:

Lung_PAYNet demonstrated superior performance compared to the UNet architecture.
Achieved a Dice similarity coefficient of 95.7%, mIOU of 91.75%, sensitivity of 92.57%, and precision of 96.75%.

Conclusions:

Lung_PAYNet offers a significant advancement in lung nodule segmentation accuracy.
The model shows great potential for improving early lung cancer diagnosis through medical image analysis.