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Anemia is a critical public health problem in developing countries, where conventional diagnostic methods are invasive and poorly accessible. This work presents a hybrid model for non-invasive anemia detection from palm images, using the public dataset “Anemia Detection Using Palpable Palm Image” from Ghana. Deep learning-based feature extractors (BoTNet, ViT, Swin Transformer, PiT, and MobileViT) were evaluated in combination with machine learning classifiers (SVM, Random Forest, k-NN, Naïve Bayes, and Decision Tree). The best performance was obtained with MobileViT + SVM, achieving 99.75% accuracy, 100% precision, 99.5% recall, an F1-score of 99.75%, and an AUC of 1.0. These results demonstrate the potential of lightweight hybrid models as an accurate and scalable alternative for early anemia detection in resource-limited settings.

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Hybrid Model for Detecting Anemia from Palm Images using Vision Transformers and Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Melissa Taipe,
  • Katherine Ascurra,
  • Wilfredo Ticona

摘要

Anemia is a critical public health problem in developing countries, where conventional diagnostic methods are invasive and poorly accessible. This work presents a hybrid model for non-invasive anemia detection from palm images, using the public dataset “Anemia Detection Using Palpable Palm Image” from Ghana. Deep learning-based feature extractors (BoTNet, ViT, Swin Transformer, PiT, and MobileViT) were evaluated in combination with machine learning classifiers (SVM, Random Forest, k-NN, Naïve Bayes, and Decision Tree). The best performance was obtained with MobileViT + SVM, achieving 99.75% accuracy, 100% precision, 99.5% recall, an F1-score of 99.75%, and an AUC of 1.0. These results demonstrate the potential of lightweight hybrid models as an accurate and scalable alternative for early anemia detection in resource-limited settings.