Non-invasive A, B and O Blood Group Identification from Ocular Images Using a Hybrid Multi-modal Deep Learning Approach

Traditional blood group identification methods, such as serological testing or fingerprint based biometric analysis, require physical contact, specialized equipment and laboratory processing. To remove these boundaries, this study proposes a novel, which is a completely contact -free approach to determine A, B and O blood groups using ocular image analysis. Unlike the previous methods that rely on fingerprint or vein pattern, our technique takes advantage of iris color, conjunctival vasculature, limbal ring intensity, and other eye field features to classify blood group types. A custom dataset of 3,000 eye images was collected from diverse demographics under different lighting conditions. The key features were extracted using hyperspectral imaging and deep learning-based segmentation. We introduce a hybrid multi-modal attention network (HMAN), which integrates transformer-based spatial encoding, convolutional feature extraction, and self-attention mechanisms to enhance classification accuracy. The proposed model obtained 97.1%accuracy, improved ResNet-50 (92.3%) and KushalNet-B4 (94.5%). Ablation studies confirmed that multi-modal feature fusion improves discriminatory capacity for blood group-specific patterns. This work establishes the first AI-operated, non-invasive blood group detection framework with emergency medical, blood donor screening, and potential applications in biometric diagnostics. Future research will focus on real-time deployment, dataset expansion, and multi-modal physiological feature integration to improve robustness. Our findings represent a major advancement in contact-free medical diagnosis, which paves the way for AI enhance hematological classification.