Background of study: Traditional blood group determination methods are time-consuming, invasive, and require specialized equipment and trained personnel, leading to delays in medical decisions in remote or emergency settings.Aims and scope of paper: This project explores an innovative, non-invasive approach to blood group detection using fingerprint recognition and image processing, aiming to overcome limitations of prior methods regarding accuracy, scalability, and accessibility. The core hypothesis is that unique fingerprint patterns can correlate with blood groups using advanced machine learning.Methods: The proposed system involves fingerprint image acquisition (via smartphone/scanner), pre-processing (noise reduction, grayscale conversion, etc.), feature extraction using ORB and GLCM, and classification with a Convolutional Neural Network (CNN). The lightweight MobileNet architecture is utilized for efficiency, trained on a self-collected dataset of 60,000 thumb images categorized into 8 blood group classes, with HOG integrated for enhanced feature extraction. The system is accessible via a user-friendly chatbot interface.Result: Experimental evaluation demonstrated robust performance across various deep learning models. ResNet50 achieved the highest accuracy of 95.3% on the BloodHub Dataset. The custom CNN model achieved 94.8% accuracy on the Custom Fingerprint Dataset, and MobileNet achieved a commendable 93.6% accuracy on the BloodCell-Detection-Dataset.Conclusion: This project presents a viable, non-invasive blood group detection method by combining fingerprint biometrics, advanced image processing, and deep learning within a chatbot interface. Deep architectures like ResNet50 and the tailored CNN consistently achieved over 94% accuracy, validating the feasibility of reagent-free, portable blood typing for emergency, rural, and resource-constrained environments. This system can democratize critical diagnostic services and enhance patient care.
Eenaja et al. (Wed,) studied this question.