[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126301-en":3,"doc-seo-126301-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126301,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",7,"Healthcare","Enhancing diagnostic accuracy in breast cancer - integrating novel machine learning approaches with enhanced image preprocessing for improved mammography analysis","This study explores computer-aided diagnosis (CAD) to improve mammography image quality and highlight potentially suspicious regions, supporting breast cancer screening where mammography is the primary modality. The objective is to determine the best preprocessing-algorithm combination to increase accuracy and strengthen subsequent classification and segmentation. Using the mini-MIAS dataset, preprocessing removes labels and pectoral muscle, then applies CLAHE, unsharp masking, and median filtering to enhance lesion visibility before k-means region extraction and feature-based machine-learning classification.","© Pol J Radiol 2024; 89: e573-e583 DOI: [https://doi.org/10.5114/pjr/195523](https://doi.org/10.5114/pjr/195523)  \n[Received: 18.08.2024](Received: 18.08.2024)  \n[Accepted: 03.11.2024](Accepted: 03.11.2024)  \n[Published: 22.12.2024](Published: 22.12.2024)  \n[http://www.polradiol.com](http://www.polradiol.com)  \nOriginal paper  \nEnhancing diagnostic accuracy in breast cancer: integrating novel machine learning approaches with enhanced image preprocessing for improved mammography analysis  \nMohsen Mehrabi1,A,B,D,E, Nafise Salek2,B,E  \n1Radiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, Iran 2Nuclear Fuel Research School, Nuclear Science and Technology Research Institute, Tehran, Iran  \nAbstract  \nPurpose: This study explored the use of computer-aided diagnosis (CAD) systems to enhance mammography image quality and identify potentially suspicious areas, because mammography is the primary method for breast cancer screening. The primary aim was to find the best combination of preprocessing algorithms to enable more precise classification and interpretation of mammography images because the selected preprocessing algorithms significantly impact the effectiveness of later classification and segmentation processes.  \nMaterial and methods: The study utilised the mini-MIAS database of mammography images and examined the impact of applying various preprocessing method combinations to differentiate between malignant and benign breast lesions. The preprocessing steps included removing label information and pectoral muscle, followed by applying algorithms such as contrast-limited adaptive histogram equalisation (CLAHE), unsharp masking (USM), and median filtering (MF) to enhance image resolution and visibility. After preprocessing, a k-means clustering technique was used to extract potentially suspicious regions, and features were then extracted from these regions of interest (ROIs) . The extracted feature datasets were classified using various machine learning algorithms, including artificial neural networks, random forest, and support vector machines.  \nResults: The findings showed that the combination ofCLAHE, USM, and MF preprocessing algorithms resulted in the highest classification performance, outperforming the use ofCLAHE alone.  \nConclusions: The integration of advanced preprocessing techniques with machine learning significantly enhances the accuracy of mammography analysis, facilitating more precise differentiation between malignant and benign breast lesions. Key words: mammography, classification, machine learning, artificial neural network, cancer.  \nIntroduction  \nMalignant diseases are a significant contributor to global mortality rates. Breast cancer stands out as the most prevalent form of cancer among women across the world [1] . Timely detection of these conditions is crucial for successful treatment outcomes. Consequently, advanced imaging techniques have been introduced to enhance the prospects of early breast cancer diagnosis. A range of modalities,  \nsuch as ultrasonography (US), mammography, and magnetic resonance imaging (MRI) are employed to identify breast cancer [2] . From these, mammography stands out as a relatively cost-effective, straightforward, expedient, and widely utilised screening tool for the early identification of breast cancer. This is because mammographic imaging can reveal even subtle alterations within the breast that might not be detectable through physical examination [3] .  \nCorrespondence address:  \nDr. Mohsen Mehrabi, Institute of Nuclear Science and Technology, Tehran, Iran, [e-mail: msmehrabi@aeoi.org.ir](e-mail: msmehrabi@aeoi.org.ir)  \nAuthors’ contribution:  \nA Study design ∙ B Data collection ∙ C Statistical analysis ∙ D Data interpretation ∙ E Manuscript preparation ∙ F Literature search ∙ G Funds collection  \nThis is an Open Access journal, all articles are distributed under the terms of the Creative Commons Attribution-Noncommercial-No ","cbCaij8vP9e2WccM","https://ap.wps.com/l/cbCaij8vP9e2WccM","pdf",256076,5,1,11,"English","en",105,"# Abstract\n## Purpose\n## Material and methods\n## Results\n## Conclusions\n# Introduction\n## Breast cancer and the role of mammography\n## Image processing and CAD for lesion differentiation\n## Microcalcifications and diagnostic challenges","[{\"question\":\"What is the primary goal of the study on mammography analysis?\",\"answer\":\"To identify the best combination of image preprocessing algorithms that enables more precise classification and interpretation of mammography images in a CAD framework.\"},{\"question\":\"Which dataset and preprocessing steps are used in the methodology?\",\"answer\":\"The mini-MIAS mammography dataset is used; preprocessing removes label information and pectoral muscle and applies CLAHE, unsharp masking, and median filtering to enhance image visibility.\"},{\"question\":\"How are suspicious regions and final predictions generated?\",\"answer\":\"k-means clustering extracts potentially suspicious regions, features are derived from regions of interest, and machine learning models (including ANN, random forest, and SVM) classify malignant versus benign lesions.\"}]","Enhancing diagnostic accuracy in breast cancer - integrating novel machine learning approaches with enhanced image preprocessing for improved mammography analysis | PDF",1785904330,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"enhancing-diagnostic-accuracy-in-breast-cancer-integrating-novel-machine-learning-approaches-with-enhanced-image-preprocessing-for-improved-mammography-analysis","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/enhancing-diagnostic-accuracy-in-breast-cancer-integrating-novel-machine-learning-approaches-with-enhanced-image-preprocessing-for-improved-mammography-analysis/126301/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-21","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the primary goal of the study on mammography analysis?","Question",{"text":77,"@type":78},"To identify the best combination of image preprocessing algorithms that enables more precise classification and interpretation of mammography images in a CAD framework.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which dataset and preprocessing steps are used in the methodology?",{"text":82,"@type":78},"The mini-MIAS mammography dataset is used; preprocessing removes label information and pectoral muscle and applies CLAHE, unsharp masking, and median filtering to enhance image visibility.",{"name":84,"@type":75,"acceptedAnswer":85},"How are suspicious regions and final predictions generated?",{"text":86,"@type":78},"k-means clustering extracts potentially suspicious regions, features are derived from regions of interest, and machine learning models (including ANN, random forest, and SVM) classify malignant versus benign lesions.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,119,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]