Distinguishing lateral malleolar avulsion fractures (LMAFs) from subfibular ossicles (SFOs) on routine ankle radiographs is a clinically consequential challenge, as the two conditions share overlapping radiographic appearances but require distinct management strategies. We developed a two-stage deep learning framework that first localizes perimalleolar bone fragments using RetinaNet and subsequently classifies them as an LMAF or an SFO using a fine-tuned MobileNetV2 classifier. Applied to X-ray images from 2,121 patients across two centers, MobileNetV2 achieved an area under the curve of 0.887 on the external test set, outperforming three comparator architectures and two experienced radiologists. Radiologists provided with AI-generated predictions and saliency maps showed significant improvement in diagnostic accuracy over unaided reading. These findings demonstrate that an integrated detection-classification pipeline can enhance first-visit radiographic triage, offering a practical, lightweight approach to support earlier and more accurate clinical decision-making in acute ankle injuries.