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arXiv Computer Vision By Kibrom Gebremedhin, Hadush Hailu, Bruk Gebregziabher, Yordanos Hailu

Comparative Performance and Parameter-Efficient Adaptation of DINOv2 for Active Trachoma Classification

Read the original on arXiv Computer Vision →

The study evaluates the performance of the DINOv2 visual representation for classifying Trachomatous Inflammation-Follicular (TF) versus normal conjunctival images. Using 1,546 images processed by the OPTED pipeline, the authors compare six pretrained backbones and then test four lightweight adaptation methods on DINOv2 ViT-B/14. The best results—91.66% accuracy, 90.69% macro‑F1, and 96.06% AUC—were achieved with DINOv2 plus Efficient Channel Attention (ECA) and a focal‑plus‑center loss, though ECA’s benefit varied with the loss function.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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