Gastrointestinal tract lesion type identification method and system
The invention provides a gastrointestinal tract lesion type identification method and system, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a to-be-detected WCE image; the method comprises the following steps: acquiring a WCE image data set, ca...
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| creator | SHU ZHI YE BO ZHA WEI WANG SHUFANG FU YINGBING ZHANG LETING WANG BO QI BOWEN |
| description | The invention provides a gastrointestinal tract lesion type identification method and system, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a to-be-detected WCE image; the method comprises the following steps: acquiring a WCE image data set, calling a ResNet50 pre-training model, adding a training attention module in the ResNet50 pre-training model, and performing visualization by using a Grad-CAM model to obtain a gastrointestinal lesion classification model; and inputting a to-be-detected WCE image into the gastrointestinal lesion classification model, and outputting a gastrointestinal lesion type identification result. According to the method, a classification method based on ResNet50 and attention module combined transfer learning is adopted, so that the precision can be remarkably improved, and the method has extremely high precision and good robustness for different lesion tissues and gastrointestinal tract images in various environments.
本发明提供一种胃肠 |
| format | Patent |
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本发明提供一种胃肠</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PHYSICS</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20231114&DB=EPODOC&CC=CN&NR=117058467A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20231114&DB=EPODOC&CC=CN&NR=117058467A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SHU ZHI</creatorcontrib><creatorcontrib>YE BO</creatorcontrib><creatorcontrib>ZHA WEI</creatorcontrib><creatorcontrib>WANG SHUFANG</creatorcontrib><creatorcontrib>FU YINGBING</creatorcontrib><creatorcontrib>ZHANG LETING</creatorcontrib><creatorcontrib>WANG BO</creatorcontrib><creatorcontrib>QI BOWEN</creatorcontrib><title>Gastrointestinal tract lesion type identification method and system</title><description>The invention provides a gastrointestinal tract lesion type identification method and system, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a to-be-detected WCE image; the method comprises the following steps: acquiring a WCE image data set, calling a ResNet50 pre-training model, adding a training attention module in the ResNet50 pre-training model, and performing visualization by using a Grad-CAM model to obtain a gastrointestinal lesion classification model; and inputting a to-be-detected WCE image into the gastrointestinal lesion classification model, and outputting a gastrointestinal lesion type identification result. According to the method, a classification method based on ResNet50 and attention module combined transfer learning is adopted, so that the precision can be remarkably improved, and the method has extremely high precision and good robustness for different lesion tissues and gastrointestinal tract images in various environments.
本发明提供一种胃肠</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHB2TywuKcrPzCtJLS7JzEvMUSgpSkwuUchJLc7Mz1MoqSxIVchMSc0ryUzLTE4sAYnlppZk5KcoJOalKBRXFpek5vIwsKYl5hSn8kJpbgZFN9cQZw_d1IL8-NTigsTk1LzUknhnP0NDcwNTCxMzc0djYtQAAOrfM1A</recordid><startdate>20231114</startdate><enddate>20231114</enddate><creator>SHU ZHI</creator><creator>YE BO</creator><creator>ZHA WEI</creator><creator>WANG SHUFANG</creator><creator>FU YINGBING</creator><creator>ZHANG LETING</creator><creator>WANG BO</creator><creator>QI BOWEN</creator><scope>EVB</scope></search><sort><creationdate>20231114</creationdate><title>Gastrointestinal tract lesion type identification method and system</title><author>SHU ZHI ; YE BO ; ZHA WEI ; WANG SHUFANG ; FU YINGBING ; ZHANG LETING ; WANG BO ; QI BOWEN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN117058467A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>SHU ZHI</creatorcontrib><creatorcontrib>YE BO</creatorcontrib><creatorcontrib>ZHA WEI</creatorcontrib><creatorcontrib>WANG SHUFANG</creatorcontrib><creatorcontrib>FU YINGBING</creatorcontrib><creatorcontrib>ZHANG LETING</creatorcontrib><creatorcontrib>WANG BO</creatorcontrib><creatorcontrib>QI BOWEN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>SHU ZHI</au><au>YE BO</au><au>ZHA WEI</au><au>WANG SHUFANG</au><au>FU YINGBING</au><au>ZHANG LETING</au><au>WANG BO</au><au>QI BOWEN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Gastrointestinal tract lesion type identification method and system</title><date>2023-11-14</date><risdate>2023</risdate><abstract>The invention provides a gastrointestinal tract lesion type identification method and system, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a to-be-detected WCE image; the method comprises the following steps: acquiring a WCE image data set, calling a ResNet50 pre-training model, adding a training attention module in the ResNet50 pre-training model, and performing visualization by using a Grad-CAM model to obtain a gastrointestinal lesion classification model; and inputting a to-be-detected WCE image into the gastrointestinal lesion classification model, and outputting a gastrointestinal lesion type identification result. According to the method, a classification method based on ResNet50 and attention module combined transfer learning is adopted, so that the precision can be remarkably improved, and the method has extremely high precision and good robustness for different lesion tissues and gastrointestinal tract images in various environments.
本发明提供一种胃肠</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
| title | Gastrointestinal tract lesion type identification method and system |
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