Breast　cancer　(BC)　significantly　contributes　to　cancer-related　mortality　in　women,　underscoring　the　criticality　of　early　detection　for　optimal　patient　outcomes.　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　optimized　using　the　Modified　Mussel　Length-based　Eurasian　Oystercatcher　Optimization　(MML-EOO)　algorithm.　The　performance　is　evaluated　using　a　variety　of　metrics,　and　a　comparative　analysis　against　conventional　methods　is　presented.　Our　experimental　results　reveal　that　the　proposed　BC　detection　framework　attains　superior　precision　rates　in　early　disease　detection,　demonstrating　its　potential　to　enhance　mammography-based　screening　methodologies.　©　The　Author(s)　2024.