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Deep Learning for Pathology: YOLOv8 with EigenCAM for Reliable Colorectal Cancer Diagnostics.

Mohamed Farsi1, Hanaa ZainEldin2, Hanaa A Sayed3,4

  • 1Department of Information Systems, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a deep learning framework for diagnosing colorectal cancer (CRC) using YOLOv8 and EigenCAM. The AI model achieved high accuracy, offering a reliable tool for pathologists.

Keywords:
cancer diagnosisdeep learning (DL)explainable artificial intelligence (XAI)

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Area of Science:

  • Computational pathology
  • Artificial intelligence in oncology
  • Deep learning for medical imaging

Background:

  • Colorectal cancer (CRC) is a leading cause of cancer mortality worldwide.
  • Accurate and timely diagnosis is crucial for effective CRC treatment.
  • Current diagnostic methods like manual histopathology face observer variability, and computational tools often lack interpretability.

Purpose of the Study:

  • To develop and validate a deep learning framework for accurate and interpretable colorectal cancer lesion classification.
  • To integrate the YOLOv8 architecture with EigenCAM for transparent AI explanations in histopathology.
  • To establish a clinically dependable foundation for AI-assisted CRC diagnosis.

Main Methods:

  • A dataset of 5000 H&E-stained colorectal tissue slides was acquired and preprocessed.
  • Five YOLOv8 variants were comparatively evaluated for multiclass lesion classification.
  • EigenCAM was employed for visualizing discriminative regions, enhancing model interpretability.
  • Statistical validation methods including Bland-Altman plots and CDFs were used to assess robustness.

Main Results:

  • The YOLOv8 XLarge model achieved 99.38% training accuracy and 96.62% testing accuracy.
  • The framework demonstrated superior performance compared to existing CNN- and Transformer-based systems.
  • EigenCAM visualizations successfully highlighted key regions driving the AI's predictions.
  • Extensive statistical validation confirmed the framework's reliability and robustness.

Conclusions:

  • The developed deep learning framework offers a precise and interpretable solution for AI-assisted colorectal cancer diagnosis.
  • This approach addresses limitations of manual assessment and current computational methods by combining high accuracy with visual explanations.
  • The framework represents a significant advancement towards the clinical deployment of AI in pathology workflows for improved CRC detection.