[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-371210-105":59,"doc-detail-371210-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","an-automatic-cervical-cell-classification-model-based-on-improved-densenet121","An automatic cervical cell classification model based on improved DenseNet121","","An improved DenseNet121-based deep convolutional network, A2SDNet121, is proposed to overcome low accuracy in cervical cell classification and support earlier cervical cancer screening. SE attention is embedded to emphasize the nucleus region and suppress redundant areas. Stem-layer kernel and pooling sizes are adapted to cervical cell image characteristics, improving local detail extraction. An Atrous Dense Block integrates global and local salient features, achieving high multi-class performance on Herlev and SIPaKMeD datasets.",{"@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/an-automatic-cervical-cell-classification-model-based-on-improved-densenet121/371210/",{"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/an-automatic-cervical-cell-classification-model-based-on-improved-densenet121/371210.png","ImageObject",300,407,{"name":92,"@type":93},"Olivia Brown","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the A2SDNet121 model address?","Question",{"text":112,"@type":113},"It addresses low accuracy in cervical cell classification, aiming to improve diagnostic support for early cervical cancer screening.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does A2SDNet121 improve feature extraction?",{"text":117,"@type":113},"It integrates an SE module to focus on the nucleus region, adjusts Stem-layer kernel and pooling sizes for cervical image characteristics, and uses Atrous Dense Blocks to capture global and local salient features.",{"name":119,"@type":110,"acceptedAnswer":120},"How accurate is A2SDNet121 on the Herlev and SIPaKMeD datasets?",{"text":121,"@type":113},"On Herlev, accuracy reaches 99.75% for two-classification and 99.14% for seven-classification. On SIPaKMeD, accuracy reaches 99.55%, 99.75%, and 99.22% for two-, three-, and five-classification tasks, respectively.","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},371210,1790816329,{"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":39,"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},16904993612988,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nAn automatic cervical cell classification model based on improved DenseNet121  \nYue Zhang, Chunyu Ning􀀍 & Wenjing Yang  \nThe cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network. Firstly, the SE module is embedded in DenseNet121 to increase the model’s focus on the nucleus region, which contains important diagnostic information, and reduce the focus on redundant information. Secondly, the sizes ofthe convolutional kernel and pooling window of the Stem layer are adjusted to adapt to the characteristics of the cervical cell images, so that the model can extract the local detailed information more effectively. Finally, the Atrous Dense Block (ADB) is constructed, and four ADB modules are integrated into DenseNet121 to enable the model to acquire global and local salient feature information. The accuracy of A2SDNet121 for two and seven-classification tasks on the Herlev dataset is 99.75% and 99.14%, respectively. The accuracy for two, three, and five-classification tasks on the SIPaKMeD dataset reaches 99.55%, 99.75% and 99.22%, respectively. Compared with other state-of-the-art algorithms, theA2SDNet121 model performs better in the multi-classification task of cervical cells, which can significantly improve the accuracy and efficiency of cervical cancer screening.  \nKeywords Cervical cells, Convolutional neural network, Image classification, Multi-scale features, Attention mechanism  \nCervical cancer is a serious health threat faced by many women in developing countries. According to the “Global Cancer Statistics 2022”1 released by the International Agency for Research on Cancer of the World Health Organization, there were 660,000 new cervical cancer cases and 350,000 deaths in 2022. In China, there were approximately 150,000 new cases of cervical cancer and nearly 60,000 deaths, accounting for 22.7% and 17.1% of the global total, respectively. As can be seen, China has about one fifth of the world’s cervical cancer patients, and the incidence and mortality rates remain high. Cervical cancer usually refers to the abnormal changes in cervical epithelial cells after infection by human papillomavirus, resulting in the formation of precancerous lesions and ultimately developing into cancer. If the disease is diagnosed in the precancerous stage or earlier, cervical cancer can be prevented and controlled more effectively.  \nCurrently, the discrimination criteria for cervical cell classification are reported by The Bethesda System (TBS)2014 version2. The specific classifications are as follows: negative for intraepithelial lesion or malignancy (NILM), atypical squamous cells of undetermined significance (ASC-US), low-grade squamous intraepithelial lesion (LSIL), atypical squamous cells, cannot exclude HSIL (ASC-H), and high-grade squamous intraepithelial lesion (HSIL) . Proper classification of cervical cells can determine the degree of cellular abnormality and pathology, which can play a key role in pre-cancer screening for cervical cancer. Therefore, cervical cell screening is an important tool in the prevention of cervical cancer and its precancerous lesions, and is essential for saving lives.  \nIn the past, cervical cell screening relied on the experience of technicians, which had the disadvantages of cumbersome, time-consuming and inefficient. Therefore, automatic screening techniques have attracted much attention3–5. At present, cervical cell automatic screening models are divided into traditional machine learning methods and deep learning methods, in terms of their","cbCaiat8AJUWgo2O","https://ap.wps.com/l/cbCaiat8AJUWgo2O","pdf",2999462,18,"English","# Introduction\n## Clinical significance of cervical cancer screening\n## Bethesda System for cervical cell classification\n## Limitations of manual and traditional machine learning methods\n## Deep learning and feature extraction challenges\n# Method\n## A2SDNet121 architecture design\n### SE module integration\n### Stem layer adjustment\n### Atrous Dense Block (ADB) construction\n# Experimental results\n## Two- and multi-classification accuracy on Herlev and SIPaKMeD","[{\"question\":\"What problem does the A2SDNet121 model address?\",\"answer\":\"It addresses low accuracy in cervical cell classification, aiming to improve diagnostic support for early cervical cancer screening.\"},{\"question\":\"How does A2SDNet121 improve feature extraction?\",\"answer\":\"It integrates an SE module to focus on the nucleus region, adjusts Stem-layer kernel and pooling sizes for cervical image characteristics, and uses Atrous Dense Blocks to capture global and local salient features.\"},{\"question\":\"How accurate is A2SDNet121 on the Herlev and SIPaKMeD datasets?\",\"answer\":\"On Herlev, accuracy reaches 99.75% for two-classification and 99.14% for seven-classification. On SIPaKMeD, accuracy reaches 99.55%, 99.75%, and 99.22% for two-, three-, and five-classification tasks, respectively.\"}]","An automatic cervical cell classification model based on improved DenseNet121 | PDF",1790191529,45]