Research on Colorectal Cancer Segmentation Algorithm Based on Deep Learning

Jing Zhao; Xiaodan Wang; Chenxi He

doi:10.62051/ijcsit.v5n1.20

Authors

Jing Zhao
Xiaodan Wang
Chenxi He

DOI:

https://doi.org/10.62051/ijcsit.v5n1.20

Keywords:

Colorectal polyp, Segmentation, Attention mechanism, Deep learning, Downsampling

Abstract

Colorectal cancer screening is important for colorectal cancer prevention and early colorectal cancer diagnosis. To address the problems of polyp color, shape, size and blurring of edges, which are common in medical images of colorectal polyps, the UNet Colorectal Cancer Segmentation Algorithm Based on Efficient Downsampling and Joint Attention Mechanism Through CIRKD (EJ-UNet-C) is proposed. Efficient Downsampling and Joint Attention Mechanism Through CIRKD, EJ-UNet).The UNet algorithm improves the downsampling part in the encoder part, which reduces the loss of information generated in the downsampling process, and obtains the E-UNet; by incorporating the joint attention module, the By adding the joint attention module, the extraction ability of polyp edges is further improved, and EJ-UNet is obtained; at the same time, EJ-UNet is used as the basic student network, and the knowledge distillation mechanism is introduced to use Deeplabv3 as the instructor's network for guided learning. The experimental results show that the optimized network EJ-UNet-C has an IOU of 0.763, which is 8.4 percentage points higher than the IOU of 0.679 of the basic network UNet. And the distillation-optimized model has a small number of parameters and high accuracy, and the overall performance of the network is excellent, which provides a reference for the research of establishing a clinical lightweight colorectal cancer image segmentation model.

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