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Lightweight Deep Learning Classification Model for Identifying Low-Resolution CT Images of Lung Cancer.

Shanmugasundaram Marappan1, Muhammad Danish Mujib2, Adnan Ahmed Siddiqui3

  • 1Department of Computer Science, College of Computer Science & Information Technology, Jazan University, Jizan, Saudi Arabia.

Computational Intelligence and Neuroscience
|September 9, 2022
PubMed
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This summary is machine-generated.

This study uses deep learning on low-resolution CT scans to identify lung cancer subtypes, Invasive Adenocarcinoma and Minimally Invasive Adenocarcinoma. The 2D DenseNet model achieved high accuracy, aiding precise diagnosis even with suboptimal image quality.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung cancer is a leading global cause of mortality, necessitating improved diagnostic tools.
  • Computed Tomography (CT) scans offer valuable information for diagnosing lung conditions.
  • Accurate identification of malignant lung nodules and their subtypes is crucial for effective treatment.

Purpose of the Study:

  • To diagnose lung cancer and assess its severity using deep learning techniques.
  • To identify and locate malignant lung nodules in CT images.
  • To categorize histological subtypes of lung cancer, specifically Invasive Adenocarcinoma (IAC) and Minimally Invasive Adenocarcinoma (MIA), using low-resolution CT scans.

Main Methods:

  • Application of deep learning, specifically DenseNet models (2D and 3D), for nodule detection and classification.

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  • Analysis of mixed ground glass nodules (mGGNs) in low-dose, low-resolution CT images with 5mm slice thickness.
  • Utilizing a dataset of 105 low-resolution CT images from 105 patients.

    • Main Results:

    • The 2D DenseNet model demonstrated superior performance compared to the 3D model.
    • Classification accuracy of 76.67%, sensitivity of 63.3%, and specificity of 100% were achieved by the 2D model.
    • Area under the receiver operating characteristic curve (AUC) was 0.88 for the 2D DenseNet model.

    Conclusions:

    • Deep learning models, particularly the 2D DenseNet, can effectively identify lung cancer histological subtypes from low-resolution CT scans.
    • The findings suggest that deep learning can aid clinicians in making more precise lung cancer diagnoses, even with non-ideal image quality.
    • This approach holds promise for improving the pathological classification of lung cancer subtypes.