International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Blood Group Detection using Fingerprint |
|---|---|
| ABSTRACT | In medical emergencies, knowing a person’s blood group rapidly could be a life-saving aspect. Traditional methods for blood group testing rely on blood withdrawal, reagents, and technical expertise, making it less feasible for emergency and distant locations. In this paper, a new technique for predicting a human blood group based on fingerprint images using deep learning techniques is proposed. Fingerprints are unique and genetically determined characteristics; hence, their feature extraction is a valuable aspect in identifying their relation to blood group traits. In this technique, a fingerprint image classification approach based on a Convolution Neural Network (CNN) for predicting a person’s blood group is discussed. A Web-based interface for image input and result presentation is also proposed. The technique could be useful in medical emergencies and could support digital healthcare, though it doesn’t replace the need for medical tests and analyses in medical settings. |
| AUTHOR | MEGHA S V, SHIVANI G D, SPANDANA K, SWAPNA M, ASHWINI M S UG Student, Dept. of CSE, GSSS Institute of Engineering and Technology for Women, Affiliated to Visvesvaraya Technological University (VTU), Mysuru, Karnataka, India Assistant Professor, Dept. of CSE, GSSS Institute of Engineering and Technology for Women, Affiliated to Visvesvaraya Technological University (VTU), Mysuru, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312038 |
| pdf/38_Blood Group Detection using Fingerprint.pdf | |
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