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To segment colorectal tumors in CT images accurately, we propose an improved U\u2010Net network for colorectal tumor image segmentation dilated\u2010pyramid\u2010attention U\u2010Net (DPA\u2010UNet). In this paper, we use the U\u2010Net network and combine techniques such as dilated convolution, weighted feature pyramid structure (W\u2010FPN), and convolutional block attention module (CBAM) mechanism. Firstly, CBAM and W\u2010FPN are combined to extract dense pixel\u2010level features for pixel labeling. Secondly, after the third network output layer, three serially dilated depth\u2010separable dilated convolutional layers with dilation rates of 1, 2, and 4, are added respectively to expand the feature map receptive field. Finally, the DPA\u2010UNet model is compared and analyzed with other new network structures. The experimental results show that DPA\u2010UNet achieves automatic segmentation of the colorectal cancer image region of interest (ROI).<\/jats:p>","DOI":"10.1002\/cpe.7670","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T12:04:11Z","timestamp":1685102651000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DPA\u2010UNet rectal cancer image segmentation based on visual attention"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-3755-0586","authenticated-orcid":false,"given":"Yuqian","family":"Wang","sequence":"first","affiliation":[{"name":"The School of Information Engineering Henan University of Science and Technology  Luoyang China"},{"name":"Department of Information Technology Tomsk Polytechnic University  Tomsk Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JianWei","family":"Ma","sequence":"additional","affiliation":[{"name":"The School of Information Engineering Henan University of Science and Technology  Luoyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Axyonov","family":"Sergey","sequence":"additional","affiliation":[{"name":"Department of Information Technology Tomsk Polytechnic University  Tomsk Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaofei","family":"Zang","sequence":"additional","affiliation":[{"name":"The School of Information Engineering Henan University of Science and Technology  Luoyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Zhang","sequence":"additional","affiliation":[{"name":"The School of Information Engineering Henan University of Science and Technology  Luoyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21601"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamasurg.2014.1756"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1159\/000446488"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3056578"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.02.060"},{"key":"e_1_2_8_8_1","article-title":"A numerical splitting and adaptive privacy budget allocation based LDP mechanism for privacy preservation in blockchain\u2010powered IoT","author":"Zhang K","year":"2022","journal-title":"IEEE Internet Things J"},{"issue":"2","key":"e_1_2_8_9_1","first-page":"263","article-title":"DEAL: differentially private auction for blockchain\u2010based microgrids energy trading","volume":"13","author":"Hassan MCJ","year":"2020","journal-title":"IEEE Trans Serv Comput"},{"issue":"2","key":"e_1_2_8_10_1","first-page":"241","article-title":"A hybrid blockchain\u2010based identity authentication scheme for multi\u2010WSN","volume":"13","author":"Cui Z","year":"2020","journal-title":"IEEE Trans Serv Comput"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3040019"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3490237"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2020.2964552"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5478"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5182"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrclinonc.2017.141"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13264"},{"key":"e_1_2_8_18_1","first-page":"396","article-title":"Segmenting MR images by level\u2010set algorithms for perspective colorectal cancer diagnosis","author":"Soomro MH","year":"2017","journal-title":"Eur Congress Comput Methods Appl Sci Eng"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2015.12.017"},{"key":"e_1_2_8_20_1","doi-asserted-by":"crossref","unstructured":"PanicJ DefeudisA MazzettiS et al.A convolutional neural network based system for colorectal cancer segmentation on MRI images. 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