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Medical Images Segmentation for Lung Cancer Diagnosis Based on Deep Learning Architectures.

Yahia Said1,2, Ahmed A Alsheikhy1, Tawfeeq Shawly3

  • 1Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an automated system for early lung cancer detection using CT scans. The AI model achieves high accuracy in segmenting and classifying tumors, aiding oncologists in faster diagnosis.

Keywords:
deep learninglung cancer classificationlung cancer segmentationmedical imagestransformers

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung cancer is a leading cause of global mortality.
  • Early diagnosis through lung image analysis is crucial for effective treatment.
  • Manual segmentation of medical images is time-consuming for clinicians.

Purpose of the Study:

  • To develop an automated system for early lung cancer diagnosis from CT scans.
  • To improve the efficiency and accuracy of lung cancer detection.
  • To provide a powerful tool for combatting lung cancer.

Main Methods:

  • A two-part system combining UNETR for segmentation and a self-supervised network for classification.
  • Utilized 3D-input CT scan data for analysis.
  • Experiments conducted on the Decathlon dataset.

Main Results:

  • Achieved state-of-the-art segmentation accuracy of 97.83%.
  • Attained classification accuracy of 98.77% for benign or malignant tumors.
  • Demonstrated the system's effectiveness in early lung cancer diagnosis.

Conclusions:

  • The proposed automated system offers a powerful tool for early lung cancer diagnosis.
  • The UNETR and self-supervised network combination enhances segmentation and classification performance.
  • This approach can significantly aid in combating lung cancer through improved diagnostic capabilities.