Deep Learning Models for Non-Invasive Blood Group Detection Using Fingerprint Images

In the context of medical diagnosis, the identification of human blood groups plays a significant role. To perform the identification of human blood groups, usually invasive identification methods are used. However, due to the limitations of the invasive methods of blood group identification, the use of fingerprint-based identification of human blood groups gained significance recently. In this paper, an accurate comparison of the recently developed deep learning models of fingerprint-based blood group identification techniques is provided. Five CNN-based models, such as ConvNeXt-Tiny, ConvNeXt-Small, EfficientNetV2-S, RepVGG-B3g4, and MobileViT-V2 models for the identification of human blood groups, are implemented. The results obtained in the experiment, considering the accuracy of the models, have proved the EfficientNetV2-S model to have the highest accuracy of 98.84%, followed by the ConvNeXt-Small, ConvNeXt-Tiny, MobileViT-V2, and RepVGG-B3g4 models with accuracies of 97.66%, 97.21%, 97.08%, and 96.98%, respectively.