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Automatic polyp image segmentation and cancer prediction based on deep learning
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, an...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9878560/ https://www.ncbi.nlm.nih.gov/pubmed/36713495 http://dx.doi.org/10.3389/fonc.2022.1087438 |
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author | Shen, Tongping Li, Xueguang |
author_facet | Shen, Tongping Li, Xueguang |
author_sort | Shen, Tongping |
collection | PubMed |
description | 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, we can find that the network of this paper has a certain degree of improvement in segmentation accuracy and operation speed, which can effectively assist physicians in removing abnormal colorectal tissues and thus reduce the probability of polyp cancer, and improve the survival rate and quality of life of patients. Also, it has good generalization ability, which can provide technical support and prevention for colon cancer. |
format | Online Article Text |
id | pubmed-9878560 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98785602023-01-27 Automatic polyp image segmentation and cancer prediction based on deep learning Shen, Tongping Li, Xueguang Front Oncol Oncology 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, we can find that the network of this paper has a certain degree of improvement in segmentation accuracy and operation speed, which can effectively assist physicians in removing abnormal colorectal tissues and thus reduce the probability of polyp cancer, and improve the survival rate and quality of life of patients. Also, it has good generalization ability, which can provide technical support and prevention for colon cancer. Frontiers Media S.A. 2023-01-12 /pmc/articles/PMC9878560/ /pubmed/36713495 http://dx.doi.org/10.3389/fonc.2022.1087438 Text en Copyright © 2023 Shen and Li https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Shen, Tongping Li, Xueguang Automatic polyp image segmentation and cancer prediction based on deep learning |
title | Automatic polyp image segmentation and cancer prediction based on deep learning |
title_full | Automatic polyp image segmentation and cancer prediction based on deep learning |
title_fullStr | Automatic polyp image segmentation and cancer prediction based on deep learning |
title_full_unstemmed | Automatic polyp image segmentation and cancer prediction based on deep learning |
title_short | Automatic polyp image segmentation and cancer prediction based on deep learning |
title_sort | automatic polyp image segmentation and cancer prediction based on deep learning |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9878560/ https://www.ncbi.nlm.nih.gov/pubmed/36713495 http://dx.doi.org/10.3389/fonc.2022.1087438 |
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