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Transfer learning based approach for lung and colon cancer detection using local binary pattern features and

Shtwai Alsubai1

  • 1Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.

Peerj. Computer Science
|April 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced Inception-ResNetV2 model with local binary patterns (LBP) for enhanced lung and colon cancer diagnosis. Achieving 99.98% accuracy, this machine learning approach promises faster, more reliable cancer detection.

Keywords:
Colon cancerLocal binary pattern featuresLungs cancerTransfer learningXAI

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cancer, particularly lung and colon cancer, poses a significant global health threat, necessitating accurate and timely diagnosis.
  • Histopathological analysis is crucial for effective cancer treatment planning.
  • Early detection of cancer significantly reduces mortality rates.

Purpose of the Study:

  • To develop and validate a machine learning model for improved accuracy in diagnosing lung and colon cancer from histopathological images.
  • To integrate deep learning with texture-based features for enhanced diagnostic performance.
  • To utilize explainable AI (XAI) techniques for model transparency.

Main Methods:

  • Implementation of the Inception-ResNetV2 deep learning architecture.
  • Incorporation of Local Binary Patterns (LBP) texture features.
  • Training and evaluation on a dataset of histopathological images.
  • Application of SHapley Additive exPlanations (SHAP) for model interpretability.

Main Results:

  • The proposed model achieved an exceptional diagnostic accuracy of 99.98%.
  • The combination of deep learning and LBP features significantly improved cancer identification.
  • SHAP analysis provided insights into the model's decision-making process, enhancing trust and transparency.

Conclusions:

  • The Inception-ResNetV2 model with LBP features demonstrates high efficacy in automated cancer diagnosis.
  • Explainable AI enhances the clinical applicability of deep learning models in oncology.
  • This approach has the potential to revolutionize cancer diagnosis, leading to more accurate and reliable medical assessments.