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Related Experiment Video

Updated: May 15, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

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

Published on: May 19, 2023

An intelligent lung nodule classification model using 3D Trans-DenseUnet++-based lung nodule segmentation.

Pavan Kumar Illa1,2, Senthil Kumar Thillaigovindan3

  • 1Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, Tamil Nadu, India. pavankumarilla7@gmail.com.

Scientific Reports
|May 13, 2026
PubMed
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This study introduces an advanced deep learning framework for lung nodule classification, significantly improving malignancy detection accuracy in CT scans. The novel IF-RTH-ADNet-LSTM model achieves 94.98% accuracy, outperforming existing methods for early lung cancer diagnosis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung nodule detection is critical for lung cancer treatment, but imbalanced datasets hinder neural network performance.
  • Accurate recognition of malignant lung nodules in CT scans is essential for early diagnosis and effective treatment.
  • Conventional methods face limitations in diagnosing malignant nodules due to challenges with low training data and feature extraction.

Purpose of the Study:

  • To develop a robust deep learning framework for accurate lung nodule classification from CT images.
  • To address the challenges of limited data and improve the performance of automated lung nodule diagnosis.
  • To enhance early lung cancer detection and support clinical decision-making.

Main Methods:

  • A three-stage framework involving image collection, segmentation using 3D Trans-DenseUnet++ (3D-TDUnet++), and classification.
Keywords:
3D Trans-DenseUnet++Adaptive DenseNet with long short-term memory layerIntensified fitness-based Red-Tailed Hawk algorithmLung nodule classification

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Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
07:53

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

Related Experiment Videos

Last Updated: May 15, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

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

Published on: May 19, 2023

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
07:53

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

  • Segmentation isolates lung nodules, reducing noise and enhancing feature extraction for improved accuracy.
  • Classification employs an Adaptive DenseNet with a Long Short-Term Memory (LSTM) layer (ADNet-LSTM), optimized by the Intensified Fitness-based Red-Tailed Hawk Algorithm (IF-RTHA).

    • Main Results:

    • The IF-RTH-ADNet-LSTM model achieved a high accuracy of 94.98% in classifying lung nodules.
    • This performance surpasses existing methods like RAN (90.21%), Densenet (92.38%), LSTM (91.68%), and ADNet-DenseNet (92.87%).
    • The framework demonstrated effectiveness in evaluating malignancy risk and providing reliable clinical decisions.

    Conclusions:

    • The developed lung nodule classification framework offers a significant advancement in automated diagnosis.
    • The IF-RTHA optimization and ADNet-LSTM classification contribute to superior accuracy and efficiency.
    • This approach enhances early lung cancer detection, reduces manual effort, and improves diagnostic consistency.