Architectural　Distortion　(AD)　is　a　common　abnormality　in　digital　mammograms,　alongside　masses　and　microcalcifications.　Detecting　AD　in　dense　breast　tissue　is　particularly　challenging　due　to　its　heterogeneous　asymmetries　and　subtle　presentation.　Factors　such　as　location,　size,　shape,　texture,　and　variability　in　patterns　contribute　to　reduced　sensitivity.　To　address　these　challenges,　we　propose　a　novel　feature　fusion-based　Vision　Transformer　(ViT)　attention　network,　combined　with　VGG-16,　to　improve　accuracy　and　efficiency　in　AD　detection.　Our　approach　mitigates　issues　related　to　texture　fixation,　background　boundaries,　and　deep　neural　network　limitations,　enhancing　the　robustness　of　AD　classification　in　mammograms.　Experimental　results　demonstrate　that　the　proposed　model　achieves　state-of-the-art　performance,　outperforming　eight　existing　deep　learning　models.　On　the　PINUM　dataset,　it　attains　0.97　sensitivity,　0.92　F1-score,　0.93　precision,　0.94　specificity,　and　0.96　accuracy.　On　the　DDSM　dataset,　it　records　0.93　sensitivity,　0.91　F1-score,　0.94　precision,　0.92　specificity,　and　0.95　accuracy.　These　results　highlight　the　potential　of　our　method　for　computer-aided　breast　cancer　diagnosis,　particularly　in　low-resource　settings　where　access　to　high-end　imaging　technology　is　limited.　By　enabling　more　accurate　and　timely　AD　detection,　our　approach　could　significantly　improve　breast　cancer　screening　and　early　intervention　worldwide.　©　2013　IEEE.