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

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.