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Automatic Detection of Colorectal Polyps Using Transfer Learning.

Eva-H Dulf1, Marius Bledea1, Teodora Mocan2,3

  • 1Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Memorandumului Str. 28, 400014 Cluj-Napoca, Romania.

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|September 10, 2021
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Summary
This summary is machine-generated.

This study introduces an AI-powered computer-aided diagnosis system for colorectal cancer, enhancing diagnostic accuracy in colonoscopies. The system aids clinicians by analyzing images and predicting outcomes, improving patient care.

Keywords:
artificial intelligencecolorectal cancercomputer aided decision support system

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Colorectal cancer is a leading cause of cancer death globally, with over 1.36 million new cases annually.
  • Current diagnostic methods face challenges in accuracy due to complex factors and increasing patient numbers.
  • Novel diagnostic tools are essential to improve the detection and management of colorectal cancer.

Purpose of the Study:

  • To develop and evaluate a computer-aided diagnosis (CADx) system for colorectal cancer detection.
  • To enhance the accuracy and efficiency of colonoscopy analysis using artificial intelligence.
  • To provide clinicians with an intuitive tool for improved decision-making in colorectal cancer diagnosis.

Main Methods:

  • Development of a computer-aided diagnosis system integrating artificial intelligence for colonoscopy image analysis.
  • Utilization of a convolutional neural network (CNN) for classifying eight tissue types.
  • Implementation of a semantic segmentation network for identifying malignant areas within colonoscopies.

Main Results:

  • The CNN achieved a sensitivity of 98.13% and an F1 score of 98.14% in tissue classification.
  • The semantic segmentation network demonstrated a Jaccard index of 75.18% in identifying malignant regions.
  • The developed application offers an intuitive interface for medical staff interaction with the AI system.

Conclusions:

  • The AI-driven CADx system shows significant potential in improving colorectal cancer diagnosis accuracy.
  • The tool can assist clinicians in making more informed decisions, potentially impacting personalized medicine.
  • Combining clinical expertise with AI offers a powerful approach to advancing colorectal cancer patient care.