Blood Group Prediction Using Fingerprint Samples

@article{1718042,
      author = {Harshitha M, Harish T, Kishan P, Likith P, Madan Y},
      title = {Blood Group Prediction Using Fingerprint Samples},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {3731-3735},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718042.pdf},
      abstract = {This project develops a non-invasive system to predict human blood group using fingerprint images and deep learning. Fingerprints from the SOCOFing dataset are preprocessed and classified using a CNN/EfficientNet-B0 model into eight blood groups (A+, A−, B+, B−, AB+, AB−, O+, O−). A Python interface enables users to upload a fingerprint and receive an instant prediction, demonstrating that fingerprint patterns can support fast blood group screening without lab tests.},
      keywords = {Blood group prediction, CNN, deep learning, EfficientNet-B0, fingerprint recognition, non-invasive, SOCOFing dataset.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1718042}
  }