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@article{188726,
        author = {N.Farzana and Dr.M.Malarvizhi and Mrs.G.Kowsalya and Mr.K.VIJAYPRABAKARAN},
        title = {BLOOD GROUP DETERMINATION USING FINGERPRINT PATTERN WITH RIDGE FREQUENCY},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {3181-3186},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188726},
        abstract = {Fingerprint-based blood group detection represents one of the innovative approaches that may revolutionize medical diagnostics by providing a non-invasive approach that is efficient and accessible compared to traditional serological methods. Some of the biggest challenges have to be addressed to give this full scope to this field. All these challenges overlap including small, biased datasets, lack of real-world validation, inconsistent methodologies, feature extraction or generalization in diverse populations, among others. Future research studies need to focus on building larger and more diverse datasets; standardize methodologies; and test the system in real-world environments in order to ensure applicability and scalability on a much broader scale. Advanced machine learning techniques, such as deep learning and interdisciplinary collaboration, will unlock innovative venues and solutions with high accuracy and robustness through the development of fingerprint-based blood group detection systems.},
        keywords = {Blood group determination, fingerprint pattern, ridge frequency, Gabor filter, Convolutional neural networks},
        month = {December},
        }