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Early Lung Cancer Detection via AI-Enhanced CT Image Processing Software.

Joel Silos-Sánchez1, Jorge A Ruiz-Vanoye1, Francisco R Trejo-Macotela1

  • 1Dirección de Investigación, Innovación y Posgrado, Universidad Politécnica de Pachuca, Carretera Pachuca-Cd. Sahagún Km 20, Ex-Hacienda de Santa Bárbara, Zempoala 43830, HGO, Mexico.

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Summary

Artificial intelligence (AI) software enhances lung cancer diagnosis by analyzing chest CT scans. This AI-enhanced system improves accuracy in detecting malignant nodules, aiding early cancer screening.

Keywords:
AI-assisted screeningCT scanDICOM imagesartificial intelligenceearly diagnosisimage preprocessinglung cancermedical image analysis

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Lung cancer is a leading cause of cancer mortality globally.
  • Early and accurate detection is crucial for improving patient outcomes.
  • Current diagnostic methods require enhancement for precision.

Purpose of the Study:

  • To explore AI-based software for lung cancer diagnosis using DICOM images.
  • To enhance image visualization, preprocessing, and diagnostic precision in chest CT scans.
  • To integrate AI into radiological workflows for early cancer screening.

Main Methods:

  • Processing DICOM images (converted to JPG/PNG) with AI software.
  • Implementing an ensemble of machine learning algorithms (Random Forest, Gradient Boosting, SVM, KNN).
  • Performing image normalization, denoising, segmentation, and feature extraction.

Main Results:

  • The AI-enhanced system showed significant improvements in diagnostic accuracy and robustness.
  • The ensemble model achieved over 90% classification accuracy in identifying lung nodules.
  • Effectiveness demonstrated in distinguishing malignant from non-malignant pulmonary nodules.

Conclusions:

  • AI-assisted CT image processing significantly aids early lung cancer detection.
  • The methodology enhances diagnostic confidence and supports clinical decision-making.
  • Represents a viable step toward integrating AI into routine radiological workflows.