Deep learning intestinal tract polyp segmentation method based on multi-scale information and parallel attention mechanism
A deep learning, multi-scale technology, applied in the field of deep learning image segmentation, can solve the problem of insufficient segmentation accuracy of intestinal polyps, and achieve the effect of shortening training time and accurate and effective classification
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[0035] In order to clarify the purpose, technical solutions and advantages of the present invention, the present invention will be further described in detail below in conjunction with specific embodiments and accompanying drawings.
[0036] refer to Figure 1 to Figure 8 , a deep learning intestinal polyp segmentation method based on multi-scale information and parallel attention mechanism, including the following steps:
[0037] Step 1: Obtain the picture to be segmented: the experimental data set of the present invention is from the public polyp data set CVC-ClinicDB, including polyp pictures of various types, shapes and colors;
[0038] Step 2: Use Res2Net deep convolutional neural network module and double compression excitation module (DoubleSqueeze and Excited, DSE) as the encoder to extract the features of the image;
[0039] Residual modules are fundamental modules in many modern backbone CNN architectures, such as figure 1 (a) shown. The Res2Net used in the presen...
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