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A Novel Texture Extraction-Based Compressive Sensing for Lung Cancer Classification.

Indrarini Dyah Irawati1, Sugondo Hadiyoso1, Gelar Budiman2

  • 1School of Applied Science, Telkom University, Bandung, Jawa Barat, Indonesia.

Journal of Medical Signals and Sensors
|February 2, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel compressive sensing (CS) method for lung cancer classification, achieving 84% accuracy and reducing memory storage needs for medical images.

Keywords:
Classificationcompressive sensingextractionsparsetexture

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Area of Science:

  • Medical Imaging
  • Computer Science
  • Signal Processing

Background:

  • Lung cancer image data requires significant storage and transmission bandwidth.
  • Compressive Sensing (CS) offers a statistical approach to signal sampling and feature extraction for compressed data.

Purpose of the Study:

  • To propose a novel texture extraction-based CS method for classifying lung cancer.
  • To classify three types of lung cancer: adenocarcinoma (ACA), squamous cell carcinoma (SCC), and benign lung cancer (N).

Main Methods:

  • A two-stage texture extraction process using CS for lung cancer classification.
  • The first stage detects benign lung cancer (N), while the second stage differentiates between ACA and SCC.

Main Results:

  • The two-stage texture extraction improved classification accuracy by an average of 84%.
  • The system demonstrated significant memory resource savings for technical storage.

Conclusions:

  • The proposed system effectively classifies lung cancer using texture extraction, CS, and K-Nearest Neighbor, achieving high accuracy.
  • The system conserves memory storage, offering potential as a clinical diagnostic decision support tool.
  • Further analysis of the proposed method's complexity is warranted.