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AI driven hybrid convolutional and transformer based deep learning architecture for precise lung nodule

R Yasir Abdullah1, C Venkatesan2, E Naresh3

  • 1Department of Artificial Intelligence and Data Science, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India. ry.aids@drmcet.ac.in.

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|January 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced image processing pipeline for enhanced lung nodule detection in computed tomography scans, improving accuracy and reducing false positives for early lung cancer screening.

Keywords:
Computed tomography imagingFeature extractionImage enhancementLung nodule detectionMorphological processing

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Pulmonary Nodule Detection

Background:

  • Subtle pulmonary nodules on computed tomography (CT) scans are challenging for manual review, impacting early lung cancer screening.
  • Accurate nodule detection is crucial for timely diagnosis and treatment planning.

Purpose of the Study:

  • To design and validate a comprehensive image enhancement and segmentation pipeline for detecting pulmonary nodules.
  • To achieve high spatial accuracy and maintain low false positive rates in nodule detection.

Main Methods:

  • The pipeline employs adaptive stretching for contrast enhancement and anisotropic diffusion for edge preservation.
  • Seed points are selected via adaptive thresholding, followed by 3D connectivity for region expansion.
  • Morphological operations are utilized for boundary refinement of detected nodules.

Main Results:

  • The proposed method achieved a mean overlap score of 0.83 and a sensitivity of 0.92 on the LIDC IDRI dataset.
  • The system demonstrated an average of 1.5 false positives per scan, outperforming baseline methods.
  • Performance was validated against reference masks on one thousand CT scans.

Conclusions:

  • Meticulous feature enhancement and shape-based refinement offer reproducible and clinically meaningful support for radiologists.
  • The developed pipeline can significantly aid in routine lung cancer screening by improving nodule detection accuracy.
  • This approach enhances the reliability of CT imaging for early detection of lung abnormalities.