[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-375491-105":59,"doc-detail-375491-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","enhancing-the-breast-histopathology-image-analysis-for-cancer-detection-using-variational-autoencoder","Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder","","A breast tissue biopsy is performed to determine whether a tumour is cancerous or benign, traditionally addressed with machine learning such as Random Forest and Support Vector Machine, and later with Artificial Neural Networks. The study introduces an approach that reconstructs histopathology images using a Variational Autoencoder (VAE) and a Denoising Variational Autoencoder (DVAE), followed by a Convolutional Neural Network (CNN) for classification. The reported model attains 73% accuracy, exceeding a custom CNN baseline. ",{"@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/enhancing-the-breast-histopathology-image-analysis-for-cancer-detection-using-variational-autoencoder/375491/",{"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/enhancing-the-breast-histopathology-image-analysis-for-cancer-detection-using-variational-autoencoder/375491.png","ImageObject",300,407,{"name":92,"@type":93},"Jiven","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the proposed method detect whether an image is cancerous or non-cancerous?","Question",{"text":112,"@type":113},"It reconstructs input histopathology images using a Variational Autoencoder (VAE) and a Denoising VAE (DVAE), then uses a Convolutional Neural Network (CNN) to classify the images as cancerous or non-cancerous.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why are VAE and DVAE used before the CNN in the architecture?",{"text":117,"@type":113},"They are used for reconstructing the images and learning a suitable latent representation, after which the CNN performs the final prediction based on that representation.",{"name":119,"@type":110,"acceptedAnswer":120},"What accuracy does the implementation report compared with a custom-built CNN?",{"text":121,"@type":113},"The implementation provides 73% accuracy, which is described as higher than the results from the authors’ custom-built CNN on their dataset.","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},375491,1790316790,{"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":52,"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},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399","Article  \nEnhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder  \nHarsh Vardhan Guleria 1, Ali Mazhar Luqmani 1, Harsh Devendra Kothari 1, Priyanshu Phukan 1, Shruti Patil 1, *, Preksha Pareek 1,*, Ketan Kotecha 1, Ajith Abraham 2 and Lubna Abdelkareim Gabralla 3  \nCitation: Guleria, H.V.; Luqmani, A.M.; Kothari, H.D.; Phukan, P.; Patil, S.; Pareek, P.; Kotecha, K.; Abraham, A.; Gabralla, L.A. Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder. Int. J. Environ. Res. Public Health 2023, 20, 4244. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ijerph20054244  \nAcademic Editor: Xudong Huang  \nReceived: 21 January 2023  \nRevised: 8 February 2023  \nAccepted: 9 February 2023  \nPublished: 27 February 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India  \n2 Faculty of Computing and Data Sciences, FLAME University, Lavale, Pune 412115, India  \n3 Department of Computer Science and Information Technology, College of Applied, Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia  \n* Correspondence: [shruti.patil@sitpune.edu.in](shruti.patil@sitpune.edu.in) (S.P.); [preksha.pareek@sitpune.edu.in](preksha.pareek@sitpune.edu.in) (P.P.)  \nAbstract: A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to classify the input histopathological images into whether they were cancerous or non-cancerous. The implementations continued to provide promising results, and then Artificial Neural Networks (ANNs) were applied for this purpose. We propose an approach for reconstructing the images using a Variational Autoencoder (VAE) and the Denoising Variational Autoencoder (DVAE) and then use a Convolutional Neural Network (CNN) model. Afterwards, we predicted whether the input image was cancerous or non-cancerous. Our implementation provides predictions with 73% accuracy, which is greater than the results produced by our custom-built CNN on our dataset. The proposed architecture will prove to be a new field of research and a new area to be explored in the field of computer vision using CNN and Generative Modelling since it incorporates reconstructions of the original input images and provides predictionson them thereafter.  \nKeywords: variational autoencoder; histopathology image; deep learning  \n1. Introduction  \nBreast cancer is indeed a type of cancer that occurs in the cell lines of a woman's breast. Breast cancer can be classified based on the visual representation of the cells. Ductal Carcinoma in Situ (DCIS) and Invasive Ductal Carcinoma (IDC) are two of the most frequently reported types. DCIS does not affect patients' everyday lives and accounts for a small fraction of occurrences. IDC is more dangerous and includes the majority of patients [1] .  \nIf breast cancer maybe found in its earliest stages, then there are many ways to perform a good treatment. As a result, having access to the appropriate screening technologies is crucial for identifying the early signs of breast cancer [2] . Three frequent imaging modalities used to test for this illness include mammography, ultrasonography, and thermography. Mammography [3] is among the most important tools for detecting breast cancer early. Ultrasound or diagnostic sonography treatments are often employed since mammography is ineffective for breasts. Tiny masses can be avoided usi","cbCaifdFmNdeaFzi","https://ap.wps.com/l/cbCaifdFmNdeaFzi","pdf",5424093,17,"English","# Introduction\n## Breast cancer overview and common subtypes\n## Early screening and imaging modalities\n## Deep learning and digital pathology foundations","[{\"question\":\"How does the proposed method detect whether an image is cancerous or non-cancerous?\",\"answer\":\"It reconstructs input histopathology images using a Variational Autoencoder (VAE) and a Denoising VAE (DVAE), then uses a Convolutional Neural Network (CNN) to classify the images as cancerous or non-cancerous.\"},{\"question\":\"Why are VAE and DVAE used before the CNN in the architecture?\",\"answer\":\"They are used for reconstructing the images and learning a suitable latent representation, after which the CNN performs the final prediction based on that representation.\"},{\"question\":\"What accuracy does the implementation report compared with a custom-built CNN?\",\"answer\":\"The implementation provides 73% accuracy, which is described as higher than the results from the authors’ custom-built CNN on their dataset.\"}]","Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder | PDF",1790215166,43]