Automated polyp localization in colon endoscopy images helps minimize human errors in localizing polyps. In our method, we use Faster Region-based Convolutional Neural Network (R-CNN) on Resnet 50 network to form a tight bounding box around the polyp. The bounding box is then used as input for the lazy snapping technique to determine polyps correctly. Three input variants—RGB images, histogram equalized images, and luminance images—are fed to the network. The output obtained from each variant is combined to form the final result. We have used the CVC-Clinical DB database, which has 612 images with 672 polyp instances for our study. Thirteen different combinations for obtaining the result are studied, and the best among them is identified. The result is evaluated for all combinations and against a state-of-the-art method for precision, recall, and F-measure. The proposed model achieves a precision of 80.51% and a recall value of 80.33%.

中文翻译：

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使用三种输入变体对基于更快区域的卷积神经网络和惰性捕捉的结肠肿瘤定位

结肠内窥镜图像中的自动息肉定位有助于最大限度地减少定位息肉时的人为错误。在我们的方法中，我们在 Resnet 50 网络上使用更快的基于区域的卷积神经网络 (R-CNN) 来围绕息肉形成一个紧密的边界框。然后将边界框用作延迟捕捉技术的输入，以正确确定息肉。三个输入变体——RGB 图像、直方图均衡图像和亮度图像——被馈送到网络。将从每个变体获得的输出组合起来形成最终结果。我们使用了 CVC-Clinical DB 数据库，它有 612 张图像和 672 个息肉实例用于我们的研究。研究了用于获得结果的 13 种不同组合，并确定了其中的最佳组合。结果针对所有组合进行评估，并针对精确度、召回率、和 F 测量。所提出的模型达到了 80.51% 的精度和 80.33% 的召回值。