  Breast cancer (BC) significantly contributes to cancer-related mortality in
women, underscoring the criticality of early detection for optimal patient
outcomes. A mammography is a key tool for identifying and diagnosing breast
abnormalities; however, accurately distinguishing malignant mass lesions
remains challenging. To address this issue, we propose a novel deep learning
approach for BC screening utilizing mammography images. Our proposed model
comprises three distinct stages: data collection from established benchmark
sources, image segmentation employing an Atrous Convolution-based Attentive and
Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via
an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet
(ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN
models are optimised using the Modified Mussel Length-based Eurasian
Oystercatcher Optimization (MML-EOO) algorithm. Performance evaluation,
leveraging multiple metrics, is conducted, and a comparative analysis against
conventional methods is presented. Our experimental findings reveal that the
proposed BC detection framework attains superior precision rates in early
disease detection, demonstrating its potential to enhance mammography-based
screening methodologies.
