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Current Medical Imaging

Author(s): Niharika Hegde, M. Shishir, S. Shashank, P. Dayananda* and Mrityunjaya V. Latte

DOI: 10.2174/2213335607999200415141427

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

Page: [3 - 15] Pages: 13

  • * (Excluding Mailing and Handling)

Abstract

Background: Colon cancer generally begins as a neoplastic growth of tissue, called polyps, originating from the inner lining of the colon wall. Most colon polyps are considered harmless but over the time, they can evolve into colon cancer, which, when diagnosed in later stages, is often fatal. Hence, time is of the essence in the early detection of polyps and the prevention of colon cancer.

Methods: To aid this endeavor, many computer-aided methods have been developed, which use a wide array of techniques to detect, localize and segment polyps from CT Colonography images. In this paper, a comprehensive state-of-the-art method is proposed and categorize this work broadly using the available classification techniques using Machine Learning and Deep Learning.

Conclusion: The performance of each of the proposed approach is analyzed with existing methods and also how they can be used to tackle the timely and accurate detection of colon polyps.

Keywords: CT Colonography (CTC), polyps, Deep Learning, Machine Learning (ML), Computer Aided Detection (CADe), CNN.

Graphical Abstract

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