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Optimized Deformable Model-based Segmentation and Deep Learning for Lung Cancer Classification.

Mamtha V Shetty1, Jayadevappa D1, Satish Tunga2

  • 1Department of Electronics & Instrumentation Engineering, JSS Academy of Technical Education, Bengaluru, VTU, India.

The Journal of Medical Investigation : JMI
|October 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized deformable model and deep learning for early lung cancer detection. The computer-aided diagnosis system achieved high accuracy in identifying and classifying lung cancer from medical images.

Keywords:
Bayesian fuzzy clusteringSea Lion OptimizationShepard Convolutional Neural NetworkWater cycle algorithmdeformable model

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung cancer is a leading cause of death worldwide, necessitating early detection for improved survival rates.
  • Interpreting medical images for lung cancer diagnosis can be challenging for clinicians.
  • Computer-aided diagnosis (CAD) systems offer potential for accurate and early cancer detection.

Purpose of the Study:

  • To develop and evaluate an optimized deformable model and deep learning approach for lung cancer detection and classification.
  • To enhance the accuracy of computer-aided diagnosis in identifying lung cancer from medical imaging.
  • To improve early diagnosis and treatment planning for lung cancer patients.

Main Methods:

  • The study employed pre-processing (median filtering), lung lobe segmentation (Bayesian fuzzy clustering), and lung cancer segmentation using a Water Cycle Sea Lion Optimization (WSLnO) based deformable model.
  • Data augmentation was utilized to improve the performance of the classification stage.
  • Lung cancer classification was performed using a Shepard Convolutional Neural Network (ShCNN) trained with the WSLnO algorithm, which combines Water Cycle Algorithm (WCA) and Sea Lion Optimization (SLnO).

Main Results:

  • The proposed WSLnO algorithm and ShCNN achieved high performance metrics.
  • The system demonstrated an accuracy of 0.9303, sensitivity of 0.9123, specificity of 0.9133, and average segmentation accuracy of 0.9091.
  • These results indicate the effectiveness of the proposed computer-aided diagnosis technique for lung cancer.

Conclusions:

  • The integrated approach of optimized deformable models and deep learning, specifically the WSLnO algorithm and ShCNN, shows significant promise for accurate lung cancer detection and classification.
  • This computer-aided diagnosis system can assist medical professionals in the early and precise identification of lung cancer.
  • The study highlights the potential of advanced AI techniques to improve patient outcomes in oncology through enhanced medical image analysis.