论文标题
通过转移学习和基于视觉的触觉传感分类结直肠癌息肉
Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing
论文作者
论文摘要
在这项研究中，为了解决当前的高早期检测失误率（CRC）息肉，我们探讨了利用转移学习和机器学习（ML）分类器（ML）分类器的潜力，以精确而敏感地对CRC息肉的类型进行分类。我们不使用常见的结肠镜图像，而是在一个基于唯一的基于视觉的表面触觉传感器（VS-TS）的3D纹理图像输出上应用了三种不同的ML算法。为了收集CRC息肉的现实纹理图像，用于培训使用的ML分类器并评估其性能，我们首先设计和加性地制造了48种具有不同硬度，类型和纹理的现实息肉幻象。接下来，使用各种统计指标对所使用的三种ML算法进行分类时的性能进行了定量评估。
In this study, to address the current high earlydetection miss rate of colorectal cancer (CRC) polyps, we explore the potentials of utilizing transfer learning and machine learning (ML) classifiers to precisely and sensitively classify the type of CRC polyps. Instead of using the common colonoscopic images, we applied three different ML algorithms on the 3D textural image outputs of a unique vision-based surface tactile sensor (VS-TS). To collect realistic textural images of CRC polyps for training the utilized ML classifiers and evaluating their performance, we first designed and additively manufactured 48 types of realistic polyp phantoms with different hardness, type, and textures. Next, the performance of the used three ML algorithms in classifying the type of fabricated polyps was quantitatively evaluated using various statistical metrics.
