[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-333466-105":59,"doc-detail-333466-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","deep-learning-approaches-for-breast-cancer-detection-in-histopathology-images-a-review","Deep learning approaches for breast cancer detection in histopathology images - A review","","Breast cancer remains a leading cause of death among women worldwide, and accurate diagnosis and staging rely heavily on histopathology analysis of breast tissue. This review surveys state-of-the-art deep learning methods for automated detection and classification of breast cancer from histopathology images, emphasizing the evolution of computer-aided approaches in research. It summarizes publicly available histopathology datasets, compares key model strengths and weaknesses, and reviews performance variability across datasets, preprocessing pipelines, and architectures. The review also discusses practical challenges, including the need for large, diverse data and interpretability of deep models.",{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/deep-learning-approaches-for-breast-cancer-detection-in-histopathology-images-a-review/333466/",{"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/deep-learning-approaches-for-breast-cancer-detection-in-histopathology-images-a-review/333466.png","ImageObject",300,407,{"name":92,"@type":93},"Hazel","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why are histopathology images important for breast cancer diagnosis and staging?","Question",{"text":112,"@type":113},"Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. It supports detection of malignant patterns directly from tissue appearance.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is the objective of this review?",{"text":117,"@type":113},"The review provides an overview of current state-of-the-art technologies for automated breast cancer detection in histopathology images using deep learning techniques.",{"name":119,"@type":110,"acceptedAnswer":120},"What challenges does the review highlight for deep learning in histopathology-based detection?",{"text":121,"@type":113},"Key challenges include the need for large and diverse datasets and improving interpretability of deep learning models.","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},333466,1790862805,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"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,"preview_page_count":4},137441390410,"https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984","Cancer Biomarkers 40 (2024) 1–25 1  \nDOI 10.3233/CBM-230251 IOS Press  \nDeep learning approaches for breast cancer detection in histopathology images: A review  \nLakshmi Priya C Va , Biju V Gb , Vinod B Ra and Sivakumar Ramachandranc ;􀀃  \na Department of Electronics and Communication Engineering, College of Engineering Trivandrum, Kerala, India b Department of Electronics and Communication Engineering, College of Engineering Munnar, Kerala, India c Department of Electronics and Communication Engineering, Government Engineering College Wayanad, Kerala, India  \nAbstract.  \nBACKGROUND: Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images.  \nOBJECTIVE: To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques.  \nMETHODS: This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, includingthe need for large and diverse datasets and the interpretability of deep learning models.  \nRESULTS: Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy andefficiency of breast cancer detection from histopathology images.  \nCONCLUSION: This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.  \nKeywords: Computer-aided detection, breast cancer, histopathology images, deep learning, Convolutional Neural Network (CNN)  \n1. Introduction  \nAccording to the World Health Organization (WHO) report on breast cancer published in 2021, an estimated 2.3 million women worldwide were diagnosed with breast cancer in 2020, and the disease registered 685,000 fatalities. Over the past five years, 7.8 million  \n􀀃 Corresponding author: Sivakumar Ramachandran, Department of Electronics and Communication Engineering, Government Engineering College Wayanad, Kerala, India. E-mail: [sivan@gecwyd.ac.in](sivan@gecwyd.ac.in).  \nwomen have been diagnosed with breast cancer, making it the most frequent cancer among humans. Ninety percent of breast cancer cases are caused by genetic abnormalities that develop with ageing and from everyday wear and tear on cells, including DNA damage and errors in copying genetic material during cell division. Several non-genetic and genetic elements, such as hormonal fluctuations, chemical exposure, and lifestyle choices like obesity and smoking, can result in defects in DNA replication, which may lead to the developmen","cbCaicppxkqnHeIP","https://ap.wps.com/l/cbCaicppxkqnHeIP","pdf",3969538,25,"English","# Introduction\n# Background and clinical importance\n## Tumor and microcalcification types\n## Screening, manual assessment, and CAD motivation\n# Overview of deep learning approaches\n## Detection and classification pipeline\n## Datasets for histopathology images\n# Strengths, weaknesses, and performance comparison\n# Challenges and future directions","[{\"question\":\"Why are histopathology images important for breast cancer diagnosis and staging?\",\"answer\":\"Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. It supports detection of malignant patterns directly from tissue appearance.\"},{\"question\":\"What is the objective of this review?\",\"answer\":\"The review provides an overview of current state-of-the-art technologies for automated breast cancer detection in histopathology images using deep learning techniques.\"},{\"question\":\"What challenges does the review highlight for deep learning in histopathology-based detection?\",\"answer\":\"Key challenges include the need for large and diverse datasets and improving interpretability of deep learning models.\"}]","Deep learning approaches for breast cancer detection in histopathology images - A review | PDF",1790002594,63]