Vaccine misinformation poses a persistent public health challenge, particularly in visual formats such as memes and infographics that combine text, imagery, and rhetorical cues. While textual misinformation has been widely studied, image-based vaccine misinformation remains comparatively underexplored due to the difficulty of interpreting multimodal signals at scale. In this work, we evaluate how effectively multimodal Large Vision-Language Models (LVLMs) can (i) directly classify vaccination stance from images and (ii) extract interpretable concept-level representations that support more reliable and transparent prediction. Using the VaxMeme dataset of 10,244 annotated images, we compare direct zero-shot LVLM inference against a hybrid framework in which classical machine learning models are trained on LVLM-extracted binary concept features. Our results show that grounding stance prediction in structured concept representations consistently outperforms direct LVLM classification, yielding accuracy improvements of approximately 10 - 17% while enabling explicit inspection of the visual and rhetorical cues driving model decisions. These findings highlight the value of concept-grounded, neuro-symbolic approaches for interpretable multimodal misinformation detection.