JSS Academy of Technical Education, Bangalore-560060, Karnataka, India.
Curr Med Imaging. 2021;17(1):3-15. doi: 10.2174/2213335607999200415141427.
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.
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.
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.
结肠癌通常始于组织的肿瘤性生长，称为息肉，起源于结肠壁的内层。大多数结肠息肉被认为是无害的，但随着时间的推移，它们可能会发展成结肠癌，而在晚期诊断出的结肠癌往往是致命的。因此，早期发现息肉和预防结肠癌至关重要。
为了辅助这项工作，已经开发出许多计算机辅助方法，这些方法使用各种技术从 CT 结肠成像中检测、定位和分割息肉。在本文中，提出了一种全面的最新方法，并使用机器学习和深度学习中可用的分类技术对这项工作进行了广泛的分类。
分析了所提出的方法与现有方法的性能，以及它们如何用于解决结肠息肉的及时和准确检测问题。
