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A Survey on Machine Learning and Deep Learning-based Computer-Aided Methods for Detection of Polyps in CT

Niharika Hegde1, M Shishir1, S Shashank1

  • 1JSS Academy of Technical Education, Bangalore-560060, Karnataka, India.

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Summary
This summary is machine-generated.

Early detection of colon polyps is crucial for preventing fatal colon cancer. This study proposes advanced computer-aided methods using machine learning and deep learning for accurate polyp detection in CT colonography images.

Keywords:
CNNCT Colonography (CTC)Computer Aided Detection (CADe)Deep LearningMachine Learning (ML)polyps

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Oncology

Background:

  • Colon cancer often develops from neoplastic growths called polyps.
  • Early detection of polyps is vital for preventing advanced, potentially fatal colon cancer.
  • Timely identification of polyps is essential for effective colon cancer prevention.

Purpose of the Study:

  • To propose a comprehensive, state-of-the-art computer-aided method for polyp detection.
  • To categorize and analyze various machine learning and deep learning techniques for polyp identification.
  • To improve the accuracy and timeliness of colon polyp detection using advanced algorithms.

Main Methods:

  • Development of a comprehensive, state-of-the-art computer-aided detection method.
  • Application of machine learning and deep learning classification techniques.
  • Analysis of polyp detection, localization, and segmentation from CT colonography images.

Main Results:

  • Performance analysis of proposed approaches against existing methods.
  • Evaluation of the effectiveness in tackling timely and accurate colon polyp detection.
  • Demonstration of advanced techniques for identifying neoplastic growths.

Conclusions:

  • The proposed methods show promise for enhancing the early detection of colon polyps.
  • Accurate and timely detection of polyps is achievable through advanced computational techniques.
  • This work contributes to the field of computer-aided diagnosis for colon cancer prevention.