Automated Non-Invasive Blood Group Prediction Using Fingerprint Data

This research paper presents a novel non-invasive method for predicting blood groups using fingerprint data, leveraging the power of Convolutional Neural Networks (CNNs) for accurate classification. This study directly addresses the limitations inherent in traditional invasive blood typing methods by offering a significantly faster, painless, and more accessible alternative. We systematically explore the performance of various CNN architectures, including AlexNet, LeNet, ResNet34, and VGG16, rigorously evaluating their effectiveness on established datasets such as SOCOFing and NIST SD4. The results obtained demonstrate a high degree of accuracy, strongly suggesting the potential for this approach to revolutionize blood group determination as a transformative diagnostic tool. Future research directions, crucial for realizing the full potential of this technology, include expanding the diversity and size of datasets used for training, conducting thorough real-world deployment and validation studies, and proactively addressing the ethical considerations associated with the use of biometric data. Proposed approach is expected to be beneficial not alone in health emergencies and rural settings, where quick, painless, and cheap means of diagnosis are desperately required but also in other branches like forensic examinations, identification tests, and humanitarian operations. Future developments could even extend its application to multi-diagnostic capabilities for complete patient assessment. The continued advancement of this technique for rectifying its limitations will surely open the pathway for fingerprint-based prediction of blood groups to revolutionize diagnostic techniques and enhance healthcare profits worldwide.