Abstract
The paper presents a framework that is aimed at addressing the large‐scale data challenge with a particular focus on early prediction of breast cancer based on mammography image data. The abundance of large‐scale medical imaging data in healthcare has opened the way to intelligent diagnostic systems through its ability to analyze and process large‐scale mammography data. The proposed framework, the metaheuristic optimization based large‐scale breast cancer detection (MO‐LSBCD) 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‐LSBCD framework is divided into three phases: (1) The CBIS‐DDSM mammography dataset consists of 10,239 image files with a size of 6 GB, but for classification, the analytical unit is the annotated region of interest (ROI), with 3568 ROI‐level abnormality records from 1566 unique patients (normal, malignant, benign, and benign‐without‐callback); (2) solving the large‐scale 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‐measure in comparison with the standard MapReduce model. Among different deep learning models tested on the CBIS‐DDSM dataset, the classification of EfficientNet‐B3 and ResNet‐50 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‐LSBCD framework can be used to provide a robust and scalable solution to large‐scale breast cancer detection to help clinicians in the early diagnosis of breast cancer and eventually help in the improved patient outcome.