Medical　image　analysis　frequently　encounters　data　scarcity　challenges.　Transfer　learning　has　been　effective　in　addressing　this　issue　while　conserving　computational　resources.　The　recent　advent　of　foundational　models　like　the　DINOv2,　which　uses　the　vision　transformer　architecture,　has　opened　new　opportunities　in　the　field　and　gathered　significant　interest.　However,　DINOv2＇s　performance　on　clinical　data　still　needs　to　be　verified.　In　this　paper,　we　performed　a　glioma　grading　task　using　three　clinical　modalities　of　brain　MRI　data.　We　compared　the　performance　of　various　pre-trained　deep　learning　models,　including　those　based　on　ImageNet　and　DINOv2,　in　a　transfer　learning　context.　Our　focus　was　on　understanding　the　impact　of　the　freezing　mechanism　on　performance.　We　also　validated　our　findings　on　three　other　types　of　public　datasets:　chest　radiography,　fundus　radiography,　and　dermoscopy.　Our　findings　indicate　that　in　our　clinical　dataset,　DINOv2＇s　performance　was　not　as　strong　as　ImageNet-based　pre-trained　models,　whereas　in　public　datasets,　DINOv2　generally　outperformed　other　models,　especially　when　using　the　frozen　mechanism.　Similar　performance　was　observed　with　various　sizes　of　DINOv2　models　across　different　tasks.　In　summary,　DINOv2　is　viable　for　medical　image　classification　tasks,　particularly　with　data　resembling　natural　images.　However,　its　effectiveness　may　vary　with　data　that　significantly　differs　from　natural　images　such　as　MRI.　In　addition,　employing　smaller　versions　of　the　model　can　be　adequate　for　medical　task,　offering　resource-saving　benefits.　Our　codes　are　available　at　https://github.com/GuanghuiFU/medical_dino_eval.