Cervical　cancer　remains　a　significant　global　health　concern.　Given　the　disparity　between　limited　medical　resources　and　the　requisite　professional　personnel,　the　coverage　of　cervical　screening　is　inadequate,　particularly　in　underdeveloped　areas.　Computer-assisted　liquid-based　cytology　diagnostic　systems　offer　favorable　solutions.　Detection　of　small　nuclei　within　a　complex　liquid-based　environment　poses　a　challenge,　exacerbated　by　the　restricted　availability　of　manual　annotations.　In　this　study,　we　propose　FuseDLAM,　a　comprehensive　computer-aided　diagnostic　system,　which　employs　enhanced　YOLOv8　with　transformers　for　rapid　localization　of　individual　squamous　epithelial　cells.　We　leverage　artificial　intelligence-generated　content　techniques　for　data　augmentation,　effectively　reducing　the　need　for　costly　manual　annotations.　By　integrating　multiple　deep　convolutional　neural　network　models　with　self-attention　mechanisms,　the　system　extracts　crucial　features　from　cell　nuclei.　These　features　are　then　fused　through　a　fully　connected　layer　to　facilitate　robust　cell　classification.　FuseDLAM　achieves　an　F1-score　of　99.3%　on　the　public　SIPaKMeD　dataset,　demonstrating　comparability　with　state-of-the-art　approaches.　It　also　proves　its　practical　applicability　in　real-world　clinical　scenarios,　achieving　an　F1-score　of　91.2　%　in　identifying　abnormal　cervical　squamous　cells.　Additionally,　ablation　experiments　in　both　datasets　validate　the　model＇s　effectiveness.　This　underscores　its　potential　for　widespread　application　in　medical　imaging　tasks.