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Related Experiment Video

Updated: Jun 30, 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

Diagnosing lung disorder and optimal classification using dense convolution (Dc)-CapsNet technique.

Se-Jung Lim1, Iyappan Perumal2, Vinoth Kumar Venkatesan3

  • 1School of Electrical and Computer Engineering, Chonnam National University, Yeosu Campus, Jeollanam-do, Gwangju, 59626, Republic of Korea.

Scientific Reports
|June 26, 2026
PubMed
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This summary is machine-generated.

This study introduces a new Deep Learning approach using Dense Convolution (DC)-CapsNet for early lung cancer detection from X-rays and CT scans. The model achieves 97.58% accuracy, significantly improving lung disorder diagnosis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung carcinoma is a leading cause of cancer deaths worldwide, often diagnosed late, complicating treatment.
  • Early detection and classification of lung disorders, including cancer, pneumonia, and tuberculosis, are crucial for effective patient management and improved survival rates.

Purpose of the Study:

  • To develop and validate a novel deep learning framework for automated lung disorder diagnosis and classification using chest X-ray and CT scan images.
  • To enhance the accuracy and efficiency of lung cancer detection through an advanced image-based approach.

Main Methods:

  • The proposed method involves image preprocessing with optimal filtering, segmentation using Augmented Profuse Clustering (APC), feature extraction via Deep CNN, and feature optimization with a Novel Evolutionary Water wave optimization algorithm.

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  • A Dense Convolution (DC)-CapsNet model is employed for the final prediction and detection of lung diseases, classifying images as normal or malignant.

    • Main Results:

    • The DC-CapsNet model achieved a high accuracy of 97.58%, surpassing existing models by 1.9%.
    • The model demonstrated excellent performance metrics, including a precision rate of 0.986, recall of 0.975, F1-score of 0.972, Dice Similarity Coefficient (DSC) of 0.995, and Jaccard Similarity Coefficient (JSC) of 0.954.
    • The proposed automated approach significantly outperformed traditional models across all evaluated performance parameters.

    Conclusions:

    • The developed deep learning-based automated image analysis framework offers a highly efficient and accurate method for diagnosing and classifying lung disorders.
    • The DC-CapsNet model shows significant potential for improving early lung cancer detection and patient risk stratification, leading to better treatment outcomes.