Breast Cancer Detection through the Introduction of Computer-Aided Diagnosis (CAD)

Abstract

Currently, the identification of any anomaly in mammography images presents a lot of challenges, counting the complexity of data patterns, the determination of suitable deep learning algorithms, and the requirement for computational efficiency. To overcome these obstacles, it is pivotal to create a strong deep learning algorithm with viable training protocols. This research centers on progressing both effectiveness and feature extraction methods for breast cancer detection from mammograms. It addresses the existing challenges while also investigating methodologies to improve algorithms performance. The objective of this framework is to execute an effective strategy for the abnormality in (mediolateral oblique (MLO) and cranial-caudal (CC) views) of 9,752 mammography images by utilizing augmentation methods. To boost diagnostic results and speed, we propose a novel computer-aided detection (CAD) framework that leverages an advanced deep learning system, which includes VGG16, EfficientNetB7, and DenseNet121. Our approach incorporates ensemble learning with convolutional neural network (CNN) models and applies transfer learning in all three architectures. The results illustrate the impressive accuracy of the proposed methods as 82.61%, 84.75%, 91.49%, and 88.89% for the VGG16, EfficientNetB7, DenseNet121, and ensemble models, respectively. This inventive framework marks a critical step forward in the early location of breast cancer, giving the potential for more precise and convenient analysis.