FAD-YOLO: a lightweight feature-refined and task-aligned framework for AIS-MIA discrimination on pulmonary CT.

Introduction: Adenocarcinoma Methods: Using YOLO12n as the baseline, we propose FAD-YOLO (Feature-refinement, Alignment, and Dynamic-sampling YOLO), a lightweight yet accurate detection framework with three task-oriented improvements: an A2C2f-FRFN module that replaces the standard feed-forward network within the Area-Attention C2f module with a Feature Refinement Feed-forward Network; a DySample dynamic upsampling module that replaces nearest-neighbor interpolation with content-aware learnable offsets; and a TADDH task-aligned dynamic detection head that combines a shared group-normalized convolution, deformable sampling in the localization branch, and layer-attention reweighting to alleviate classification-localization misalignment. Results: On an independent internal test set of 317 images, FAD-YOLO achieved a precision of 93.8%, a recall of 93.4%, an mAP@50 of 93.6%, and an mAP@50-95 of 70.8%, while reducing parameters by 18.7% relative to the baseline at the cost of a 12.5% increase in GFLOPs (from 6.4 to 7.2). Under identical settings, it performed comparably to or better than YOLOv5n, YOLOv8n, YOLO11n, and RT-DETR-R18, and outperformed the much larger RT-DETR-R50 on all accuracy metrics despite using approximately one-twentieth of its parameters. On an external Mendeley Data test set, the model achieved mAP@50 = 91.7% and mAP@50-95 = 68.2% without any fine-tuning. Discussion: FAD-YOLO achieves a favorable balance among accuracy, lightweight design, and cross-dataset generalization, and may serve as a candidate for further prospective validation as an aid to radiologists in AIS/MIA discrimination on resource-constrained clinical devices.