{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-algorithms-for-breast-cancer","title":"Machine Learning Algorithms for Breast Cancer Detection in Mammography Images: A Comparative Study","arxiv_id":null,"date":"2021-04-26","proceeding":"23rd International Conference on Enterprise Information Systems (ICEIS) 2021 4","authors":["Rhaylander Mendes de Miranda Almeida","Dehua Chen","Agnaldo Lopes da Silva Filho","Wladmir Cardoso Brandao"],"abstract":"Breast tumor is the most common type of cancer in women worldwide, representing approximately 12% of\r\nreported new cases and 6.5% of cancer deaths in 2018. Mammography screening are extremely important for\r\nearly detection of breast cancer. The assessment of mammograms is a complex task with significant variability\r\ndue to professional experience and human errors, an opportunity for assisting tools to improve both reliability\r\nand accuracy. The usage of deep learning in medical image analysis have increased, assisting specialists in\r\nearly detection, diagnosis, treatment or prognosis of diseases. In this article, we compare the performance of\r\nXGBoost and VGG16 in the task of breast cancer detection by using digital mammograms from CBIS-DDSM\r\ndataset. In addition, we perform a comparison of prediction accuracy between full mammogram images\r\nand patches extracted from original images based on ROI annotated by experts. Moreover, we also perform\r\nexperiments with transfer learning and data augmentation to exploit data diversity, and the ability to extract\r\nfeatures and learn from raw unprocessed data. Experimental results show that XGBoost achieves 68.29% in\r\nAUC, while VGG16 achieves approximately the same performance of 68.24% in AUC","url_abs":"https://pdfs.semanticscholar.org/9131/a8fe454ece5c207a41c9e594b3435f08824c.pdf","url_pdf":"https://pdfs.semanticscholar.org/9131/a8fe454ece5c207a41c9e594b3435f08824c.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"breast-cancer-detection","task_name":"Breast Cancer Detection"},{"task_slug":"cancer-no-cancer-per-image-classification","task_name":"Cancer-no cancer per image classification"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"prognosis","task_name":"Prognosis"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/cancer-no-cancer-per-image-classification-on","task":"Cancer-no cancer per image classification","dataset":"CBIS-DDSM","model":"XGBoost","rank_in_archive_order":15,"of":16,"metrics":{"AUC":"0.6849"},"uses_additional_data":false},{"leaderboard":"/sota/cancer-no-cancer-per-image-classification-on","task":"Cancer-no cancer per image classification","dataset":"CBIS-DDSM","model":"VGG16","rank_in_archive_order":16,"of":16,"metrics":{"AUC":"0.6822"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}