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Artificial intelligence can support this goal by enabling rapid, stage-appropriate clinical protocols. Monitoring therapy and recovery is also important across individual, family, and generational levels. This study presents a non-invasive approach using mammograms to detect breast abnormalities, classify disorders, and determine cancerous versus benign tumor tissue. Using supervised learning on a balanced, pre-processed dataset, the work evaluates decision tree (DT) and random forest (RF) classifiers over multiple features, reporting DT and RF performance results of 95.32% and 98.83%.",{"@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/automated-system-utilizing-non-invasive-technique-mammograms-for-breast-cancer-detection/328481/",{"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/automated-system-utilizing-non-invasive-technique-mammograms-for-breast-cancer-detection/328481.png","ImageObject",300,407,{"name":92,"@type":93},"Oliver","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-21",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},"What is the objective of the study on mammograms?","Question",{"text":112,"@type":113},"The study outlines a non-invasive method using mammograms to detect breast abnormalities, classify breast disorders, and identify cancerous versus benign tumor tissue.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which supervised learning models are used for classification?",{"text":117,"@type":113},"The study uses supervised learning algorithms including decision tree (DT) and random forest (RF) classifiers.",{"name":119,"@type":110,"acceptedAnswer":120},"How was the dataset prepared for training the models?",{"text":121,"@type":113},"The dataset was pre-processed so that the number of samples was balanced, unlike previous work on the same 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},328481,1790491897,{"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":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Ammar etal. BMC Medical Imaging (2024) 24:205 [https://doi.org/10.1186/s12880-024-01363-9](https://doi.org/10.1186/s12880-024-01363-9)  \nBMC Medical Imaging  \n RESEARCH Open Access  \nAutomated system utilizing non-invasive technique mammograms for breast cancer detection  \nHazem M. Ammar 1,2, Ashraf F. Tammam2*, Ibrahim M. Selim3 and Mohamed Eassa 1,4  \nAbstract  \nIn order to increase the likelihood of obtaining treatment and achieving a complete recovery, early illness identification and diagnosis are crucial. Artificial intelligence is helpful with this process by allowing us to rapidly start the necessary protocol for treatment in the early stages of disease development. Artificial intelligence is a major contributor to the improvement of medical treatment for patients. In order to prevent and foresee this problem on the individual, family, and generational levels, Monitoring the patient’s therapy and recovery is crucial. This study’s objective is to outline a non-invasive method for using mammograms to detect breast abnormalities, classify breast disorders, and identify cancerous or benign tumor tissue in the breast. We used classification models on a dataset that has been pre-processed so that the number of samples is balanced, unlike previous work on the same dataset. Identifying cancerous or benign breast tissue requires the use of supervised learning techniques and algorithms, such as random forest (RF) and decision tree (DT) classifiers, to examine up to thirty features, such as breast size, mass, diameter, circumference, and the nature of the tumor (solid or cystic) . To ascertain if the tissue is malignant or benign, the examination’s findings are employed. These features are mostly what determines how effectively anything may be categorized. The DT classifier was able to get a score of 95 . 32%, while the RF satisfied a far higher 98.83 percent.  \nKeywords Breast cancer, Decision tree, Supervised learning algorithms, Classification, Random forest  \nIntroduction  \nCancer of the breast occurs when cells in the breast spread uncontrollably manner. Cancer of the breast has several different forms, depending on which breast cell type transforms into cancerous. It’s important to  \n*Correspondence:  \nAshraf F. Tammam [ashraf.tammam@aast.edu](ashraf.tammam@aast.edu)  \n1 Computer Science Department, Thebes Higher Institute for Engineering, Cairo, Egypt  \n2 Computer Engineering Department, Faculty of Engineering and Technology, Arab Academy for Science, Technology & Maritime Transport (AASTMT), Cairo, Egypt  \n3 Faculty of Computers and Artificial Intelligence, Sadat City University, Sadat City, Egypt  \n4 Computer Science Department, Faculty of Information Systems and Computer Science, October 6 University, Giza, Egypt  \nremember that breast cancer can start anywhere in the breast. Lobules, ducts, and connective tissue make up the breast. The lobules contain the milk-secreting glands. Tubes called ducts transport milk from the mammary glands to the nipple. Connective tissue is made up of both fibrous and fatty tissue, and it’s responsible for keeping everything together. In most cases, the ducts or lobules are the starting point for breast cancer. Through the lymphatic and circulatory systems, Breast cancer has the potential to spread to other parts of the body or become uncontrollable. Breast cancer \"metastasized\" significantly, which indicates that it spread to different bodily parts [1]. The two most frequently detected kinds of breast cancer are invasive ductal carcinoma and invasive lobular carcinoma. When invasive ductal carcinoma affects the breast ducts, the cancer cells first develop inside the ducts  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the","cbCaiq4xQajsP05u","https://ap.wps.com/l/cbCaiq4xQajsP05u","pdf",1750210,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What is the objective of the study on mammograms?\",\"answer\":\"The study outlines a non-invasive method using mammograms to detect breast abnormalities, classify breast disorders, and identify cancerous versus benign tumor tissue.\"},{\"question\":\"Which supervised learning models are used for classification?\",\"answer\":\"The study uses supervised learning algorithms including decision tree (DT) and random forest (RF) classifiers.\"},{\"question\":\"How was the dataset prepared for training the models?\",\"answer\":\"The dataset was pre-processed so that the number of samples was balanced, unlike previous work on the same dataset.\"}]","Automated system utilizing non-invasive technique mammograms for breast cancer detection | PDF",1789979953,23]