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Visual polyp categorization is challenging due to varying lighting conditions, so computer-aided classification is used to improve identification of colorectal cancer–related lesions. This work proposes the Enhanced Scattering Wavelet Convolutional Neural Network (ESWCNN), combining a Convolutional Neural Network with a Scattering Wavelet Transform to jointly learn wavelet spectral features and spatial image features. Experiments on colonoscopy datasets evaluate three-class (adenoma, hyperplastic, serrated) and two-class (positive, negative) performance using n-fold cross-validation, reaching high accuracies and sensitivity/specificity while outperforming representative CNN baselines.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/colonoscopy-polyp-classification-via-enhanced-scattering-wavelet-convolutional-neural-network/332601/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/colonoscopy-polyp-classification-via-enhanced-scattering-wavelet-convolutional-neural-network/332601.png","ImageObject",300,407,{"name":92,"@type":93},"Rowan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is automated colonoscopy polyp classification needed?","Question",{"text":112,"@type":113},"Visual classification is difficult because different polyps appear under varying lighting conditions. Automated approaches help identify polyps that may develop into cancer and support screening using colonoscopy.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does ESWCNN improve polyp classification performance?",{"text":117,"@type":113},"ESWCNN combines a CNN with a Scattering Wavelet Transform. It concatenates simultaneously learnable image filters and wavelet filters to capture both common spectral features across scales/orientations and spatial features that wavelets may miss.",{"name":119,"@type":110,"acceptedAnswer":120},"What classification results are reported for ESWCNN?",{"text":121,"@type":113},"For three-class classification (adenoma, hyperplastic, serrated), the reported correct classification rates are 96.2%, 98.71%, and 97.9%, respectively. For two-class classification (positive/negative), the reported accuracy is 94.8%, with average sensitivity of 96.7% and specificity of 93.1%.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},332601,1790077765,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1099514067415,"https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502","PLOS ONE  \nOPEN ACCESS  \nCitation: Tan J, Yuan J, Fu X, Bai Y (2024)  \nColonoscopy polyp classification via enhanced scattering wavelet Convolutional Neural Network. PLoS ONE 19(10): e0302800 . [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1371/journal.pone](10.1371/journal.pone).0302800  \nEditor: Jude Hemanth, Karunya Institute of  \nTechnology and Sciences, INDIA Received: April 19, 2024  \nAccepted: August 26, 2024  \nPublished: October 11, 2024  \nCopyright: © 2024 Tan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: 1,UCI public dataset: Mesejo, P., Pizarro, D., Abergel, A., Rouquette, O., Beorchia, S., Poincloux, L., Bartoli, A. (2016) . Computer-aided classification of gastrointestinal lesions in regular colonoscopy. IEEE Transactions on Medical Imaging. [http://www.depeca.uah.es/](http://www.depeca.uah.es/)[ ](http://www.depeca.uah.es/)[colonoscopy_dataset/](colonoscopy_dataset/ 2)[ 2](colonoscopy_dataset/ 2), PloyGen data set Ali S, Dmitrieva M, Ghatwary N, Bano S, Polat G, Temizel A, et al. Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy. Medical Image Analysis. 2021:102002 . [https://www.synapse.org/](https://www.synapse.org/)[\\#](#) 3,GDZY Data cannot be  \nRESEARCH ARTICLE  \nColonoscopy polyp classification via enhanced scattering wavelet Convolutional Neural Network  \nJun Tan1,2☯*, Jiamin Yuan3,4☯, Xiaoyong Fu1, Yilin Bai1,5  \n1 School of Mathematics, Sun Yat-Sen University, Guangzhou, Guangdong, China, 2 Guangdong Province Key Laboratory of Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong, China, 3 Health construction administration center, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China, 4 The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine(TCM), Guangzhou, Guangdong, China, 5 China Southern Airlines, Guangzhou, Guangdong, China  \n☯ These authors contributed equally to this work.  \n* [mcstj@mcstj.sysu.edu.cn](mcstj@mcstj.sysu.edu.cn)  \nAbstract  \nAmong the most common cancers, colorectal cancer (CRC) has a high death rate. The best way to screen for colorectal cancer (CRC) is with a colonoscopy, which has been shown to lower the risk of the disease. As a result, Computer-aided polyp classification technique is applied to identify colorectal cancer. But visually categorizing polyps is difficult since different polyps have different lighting conditions. Different from previous works, this article presents Enhanced Scattering Wavelet Convolutional Neural Network (ESWCNN), a polyp classification technique that combines Convolutional Neural Network (CNN) and Scattering Wavelet Transform (SWT) to improve polyp classification performance. This method concatenates simultaneously learnable image filters and wavelet filters on each input channel. The scattering wavelet filters can extract common spectral features with various scales and orientations, while the learnable filters can capture image spatial features that wavelet filters may miss. A network architecture for ESWCNN is designed based on these principles and trained and tested using colonoscopy datasets (two public datasets and one private dataset) . An n-fold cross-validation experiment was conducted for three classes (adenoma, hyperplastic, serrated) achieving a classification accuracy of 96 .4%, and 94.8% accuracy in two-class polyp classification (positive and negative) . In the three-class classification, correct classification rates of 96 .2% for adenomas, 98.71% for hyperplastic polyps, and 97.9% for serrated polyps were achieved. The proposed method in the two-class experiment reached an average sensitivity of 96 .7% with 93. 1% specificity. Furthermore, we compare the performance","cbCaijD6McerpKST","https://ap.wps.com/l/cbCaijD6McerpKST","pdf",6407596,35,"English","# Abstract\n# Introduction","[{\"question\":\"Why is automated colonoscopy polyp classification needed?\",\"answer\":\"Visual classification is difficult because different polyps appear under varying lighting conditions. Automated approaches help identify polyps that may develop into cancer and support screening using colonoscopy.\"},{\"question\":\"How does ESWCNN improve polyp classification performance?\",\"answer\":\"ESWCNN combines a CNN with a Scattering Wavelet Transform. It concatenates simultaneously learnable image filters and wavelet filters to capture both common spectral features across scales/orientations and spatial features that wavelets may miss.\"},{\"question\":\"What classification results are reported for ESWCNN?\",\"answer\":\"For three-class classification (adenoma, hyperplastic, serrated), the reported correct classification rates are 96.2%, 98.71%, and 97.9%, respectively. For two-class classification (positive/negative), the reported accuracy is 94.8%, with average sensitivity of 96.7% and specificity of 93.1%.\"}]","Colonoscopy polyp classification via enhanced scattering wavelet Convolutional Neural Network | PDF",1789998885,88]