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The abundance of large\u2010scale medical imaging data in healthcare has opened the way to intelligent diagnostic systems through its ability to analyze and process large\u2010scale mammography data. The proposed framework, the metaheuristic optimization based large\u2010scale breast cancer detection (MO\u2010LSBCD) framework provides a complete solution to the extraction of discriminative features that can be used to predict the disease correctly. Early and accurate diagnosis of breast cancer is one of the most critical factors in improving survival rates and reducing treatment costs for patients. The MO\u2010LSBCD framework is divided into three phases: (1) The CBIS\u2010DDSM mammography dataset consists of 10,239 image files with a size of 6\u2009GB, but for classification, the analytical unit is the annotated region of interest (ROI), with 3568 ROI\u2010level abnormality records from 1566 unique patients (normal, malignant, benign, and benign\u2010without\u2010callback); (2) solving the large\u2010scale data problem by implementing an optimized MapReduce model to efficiently distribute and process the large volumes of breast cancer mammography images in parallel; and (3) effective deep learning techniques to classify breast cancer pathology. The experimental findings indicate that the enhanced MapReduce model reduces total processing time and improves F\u2010measure in comparison with the standard MapReduce model. Among different deep learning models tested on the CBIS\u2010DDSM dataset, the classification of EfficientNet\u2010B3 and ResNet\u201050 was better with an accuracy of 99.64% and 98.91%, respectively, compared with the other models on all evaluation metrics. The results of this paper confirm that MO\u2010LSBCD framework can be used to provide a robust and scalable solution to large\u2010scale breast cancer detection to help clinicians in the early diagnosis of breast cancer and eventually help in the improved patient outcome.<\/jats:p>","DOI":"10.1155\/ijbi\/8325888","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T09:26:22Z","timestamp":1790760382000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Big Data Analytics Framework for Early Breast Cancer Detection Using Optimized MapReduce and Deep Learning on Large\u2010Scale Mammographic Data"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6187-6012","authenticated-orcid":false,"given":"Mohammed M.","family":"Ahmed","sequence":"first","affiliation":[{"name":"Faculty of Computing and Information Technology Sohar University  Sohar City Oman  soharuni.edu.om"},{"name":"Faculty of Computers and Artificial Intelligence University of Sadat City  Sadat City Egypt  usc.edu.eg"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0993-4067","authenticated-orcid":false,"given":"Ehab","family":"Hassanien","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence Cairo University  Giza City Egypt  cu.edu.eg"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"e_1_2_11_1_2","first-page":"68514","article-title":"An IoT-Based Greenhouse Monitoring System Using Raspberry Pi","volume":"8","author":"Gao P.","year":"2020","journal-title":"IEEE Access"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1799-6"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2010.05.010"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.01.00"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13193140"},{"key":"e_1_2_11_6_2","first-page":"767","article-title":"Toward Cloud-Based Big Data Analytics Framework for Cyber-Physical-Social Systems","volume":"11","author":"Jia S.","year":"2015","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"e_1_2_11_7_2","first-page":"137","volume-title":"Proceedings of the 2020 IEEE 8th International Conference on Smart Energy Grid Engineering (SEGE)","author":"Ramya J.","year":"2020"},{"key":"e_1_2_11_8_2","first-page":"3785","article-title":"Secure Internet of Things (IoT) Infrastructure: Issues, Challenges, and Solutions","volume":"94","author":"Sundararajan S.","year":"2017","journal-title":"Wireless Personal Communications"},{"key":"e_1_2_11_9_2","first-page":"182","volume-title":"Proceedings of IEEE World Congress on Services","author":"Grolinger K.","year":"2014"},{"key":"e_1_2_11_10_2","first-page":"1","volume-title":"Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications","author":"Parker C.","year":"2012"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-19616-8"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2017.12.007"},{"key":"e_1_2_11_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13071238"},{"key":"e_1_2_11_14_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1281922"},{"key":"e_1_2_11_15_2","article-title":"IoT Big Data Analytics in Smart Cities: A Review and Challenges","volume":"21","author":"Al-Maashri H.","year":"2021","journal-title":"Sensors"},{"key":"e_1_2_11_16_2","first-page":"66316","article-title":"A Survey on Big Data Analytics for IoT Systems","volume":"6","author":"Yu S.","year":"2018","journal-title":"IEEE Access"},{"key":"e_1_2_11_17_2","first-page":"2295","article-title":"A Survey on IoT Data Analytics: State-of-the-Art and Research Challenges","volume":"5","author":"Abbasi A. 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