<![CDATA[Anemia is a prevalent health condition requiring timely detection for appropriate clinical management. Conventional diagnosis relies on blood-based examinations, which are invasive and may be less practical for rapid and accessible screening. These limitations highlight the need for non-invasive and automated approaches to support preliminary anemia screening. This study develops and evaluates a non-invasive automated anemia classification framework using fingernail images and EfficientNetV2-B0. A dataset of 4,260 fingernail images from anemia and non-anemia classes was used. The images were converted into RGB format and resized to a resolution of 224 × 224 pixels, normalized using ImageNet statistics, and augmented to improve robustness. EfficientNetV2-B0 was pre-trained on ImageNet and subsequently fine-tuned to perform binary classification. The model achieved 97.89% accuracy, 0.98 precision, 0.97 recall, 0.98 F1-score, and 0.99 ROC-AUC on the independent test set. These findings suggest that the proposed framework could support rapid and non-invasive automated anemia screening.]]>
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