Automatic polyp image segmentation and cancer prediction based on deep learning - Shen, Tongping; Li, Xueguang (Frontiers in Oncology, 2023)
The similar shape and texture of colonic polyps and normal mucosal tissues lead to low accuracy of medical image segmentation algorithms. To solve these problems, we proposed a polyp image segmentation algorithm based on deep learning technology, which combines a HarDNet module, attention module, and multi-scale coding module with the U-Net network as the basic framework, including two stages of coding and decoding. In the encoder stage, HarDNet68 is used as the main backbone network to extract features using four null space convolutional pooling pyramids while improving the inference speed and computational efficiency; the attention mechanism module is added to the encoding and decoding network; then the model can learn the global and local feature information of the polyp image, thus having the ability to process information in both spatial and channel dimensions, to solve the problem of information loss in the encoding stage of the network and improving the performance of the segmentation network. Through comparative analysis with other algorithms, the segmentation network algorithm proposed in this paper achieves relatively excellent segmentation performance on five polyp datasets. Also, it has good generalization ability, which can provide technical support and prevention for colon cancer.