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Original Article
Enhanced Blood Group Prediction with Fingerprint Images using Deep Learning
Dr Kamalraj T1
Harshitha M2
Bhagyashree3
Aishwarya BC4
Aishwarya RS5
1 2 3 4 5 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India.
Published Online: November-December 2025
Pages: 87-97
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250506014
References
[1] J. Yadav et al., “Blood Group Prediction Using Deep Learning-Based Image Processing,” International Journal of Medical Research & Health Sciences, vol. 8, pp. 123–131, 2022.
[2] Sharma, P. Kumar, and S. Thakur, “ABO and Rh Blood Group Detection Using Image Processing Techniques,” IEEE Access, vol. 10, pp. 76234–76243, 2022.
[3] Wang, Y. Zhang, and X. Li, “Automated Blood Group Typing Based on Deep Learning Models,” IEEE Transactions on Biomedical Engineering, vol. 68, pp. 562–570, 2021.
[4] M. S. Lee et al., “Machine Learning Models for Noninvasive Blood Group Prediction from Palm Images,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 5, pp. 1298–1305, 2020.
[5] K. N. Ujgare, A. Verma, P. K. Verma, and N. P. Singh, “N-BGP (Noninvasive Blood Group Prediction Dataset),” IEEE DataPort, 2023, doi: 10.21227/81ps-bx03.
[6] R. Pimenta et al., “Microfluidic Image-Based Blood Grouping and Crossmatching Systems,” IEEE Sensors Journal, vol. 22, no. 8, pp. 8374–8381, 2022.
[7] N. U. Jang, “Spectroscopic and Image-Based Blood Group Typing in Medical Diagnostics,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 8, 2023.
[8] J. Song and T. W. Han, “Blood Typing Automation via Neural Networks and Image Recognition,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, pp. 1235–1242, 2021.
[9] S. Gupta et al., “Convolutional Neural Networks for Spectroscopic Image-Based Blood Group Classification,” IEEE Transactions on Image Processing, vol. 29, pp. 5682–5690, 2020.
[10] L. K. Chan et al., “Enhanced Accuracy in Blood Group Detection Using SIFT and ORB Algorithms,” International Research Journal of Engineering and Technology (IRJET), vol. 11, pp. 98–100, 2024.
[11] F. A. Zaki and M. A. Rahman, “Artificial Intelligence and Blood Group Identification,” IEEE International Conference on Biomedical Engineering (ICBE), 2022, pp. 234–237.
[12] R. Pimenta and J. F. Almeida, “Development of a Scalable Blood Typing System Using Image Processing,” IEEE Transactions on Automation Science and Engineering, vol. 18, pp. 202–213, 2021.
[13] M. S. Melur et al., “Image Analysis for Blood Grouping Using Microfluidics and Spectroscopy,” IEEE Reviews in Biomedical Engineering, vol. 15, pp. 136–147, 2023.
[14] Fernandes et al., “Identifying Blood Groups with Spectrophotometric Methods,” IEEE Transactions on Medical Imaging, vol. 40, pp. 503–514, 2021.
[15] S. K. Saini et al., “CNN-Based Blood Group Detection Using Capillary Imaging,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 12, pp. 4275–4284, 2021.
[16] S. Tripathi and R. Garg, “Advances in Blood Group Detection Technology Through Image-Based Approaches,” IEEE Access, vol. 8, pp. 54473–54485, 2020.
[17] S. Mukherjee et al., “Using CNNs for Blood Type Prediction from Spectroscopic Images,” IEEE Xplore, 2021.
[18] Patel, “A Deep Learning Approach for Blood Type Classification Using Image Analysis,” IEEE Conference on Biomedical Engineering, pp. 123–128, 2020.
[19] M. H. Shaikh, “Detection of Blood Group Using Noninvasive Spectroscopic Image Processing,” IEEE Sensors Applications Symposium (SAS), 2021, pp. 67–71.
[20] S. Bajaj, A. Goyal, and D. Thakur, “Machine Learning for Blood Group Classification Based on Antigen Pattern Imaging,” IEEE International Conference on Machine Learning (ICML), pp. 456–460, 2021.
[2] Sharma, P. Kumar, and S. Thakur, “ABO and Rh Blood Group Detection Using Image Processing Techniques,” IEEE Access, vol. 10, pp. 76234–76243, 2022.
[3] Wang, Y. Zhang, and X. Li, “Automated Blood Group Typing Based on Deep Learning Models,” IEEE Transactions on Biomedical Engineering, vol. 68, pp. 562–570, 2021.
[4] M. S. Lee et al., “Machine Learning Models for Noninvasive Blood Group Prediction from Palm Images,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 5, pp. 1298–1305, 2020.
[5] K. N. Ujgare, A. Verma, P. K. Verma, and N. P. Singh, “N-BGP (Noninvasive Blood Group Prediction Dataset),” IEEE DataPort, 2023, doi: 10.21227/81ps-bx03.
[6] R. Pimenta et al., “Microfluidic Image-Based Blood Grouping and Crossmatching Systems,” IEEE Sensors Journal, vol. 22, no. 8, pp. 8374–8381, 2022.
[7] N. U. Jang, “Spectroscopic and Image-Based Blood Group Typing in Medical Diagnostics,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 8, 2023.
[8] J. Song and T. W. Han, “Blood Typing Automation via Neural Networks and Image Recognition,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, pp. 1235–1242, 2021.
[9] S. Gupta et al., “Convolutional Neural Networks for Spectroscopic Image-Based Blood Group Classification,” IEEE Transactions on Image Processing, vol. 29, pp. 5682–5690, 2020.
[10] L. K. Chan et al., “Enhanced Accuracy in Blood Group Detection Using SIFT and ORB Algorithms,” International Research Journal of Engineering and Technology (IRJET), vol. 11, pp. 98–100, 2024.
[11] F. A. Zaki and M. A. Rahman, “Artificial Intelligence and Blood Group Identification,” IEEE International Conference on Biomedical Engineering (ICBE), 2022, pp. 234–237.
[12] R. Pimenta and J. F. Almeida, “Development of a Scalable Blood Typing System Using Image Processing,” IEEE Transactions on Automation Science and Engineering, vol. 18, pp. 202–213, 2021.
[13] M. S. Melur et al., “Image Analysis for Blood Grouping Using Microfluidics and Spectroscopy,” IEEE Reviews in Biomedical Engineering, vol. 15, pp. 136–147, 2023.
[14] Fernandes et al., “Identifying Blood Groups with Spectrophotometric Methods,” IEEE Transactions on Medical Imaging, vol. 40, pp. 503–514, 2021.
[15] S. K. Saini et al., “CNN-Based Blood Group Detection Using Capillary Imaging,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 12, pp. 4275–4284, 2021.
[16] S. Tripathi and R. Garg, “Advances in Blood Group Detection Technology Through Image-Based Approaches,” IEEE Access, vol. 8, pp. 54473–54485, 2020.
[17] S. Mukherjee et al., “Using CNNs for Blood Type Prediction from Spectroscopic Images,” IEEE Xplore, 2021.
[18] Patel, “A Deep Learning Approach for Blood Type Classification Using Image Analysis,” IEEE Conference on Biomedical Engineering, pp. 123–128, 2020.
[19] M. H. Shaikh, “Detection of Blood Group Using Noninvasive Spectroscopic Image Processing,” IEEE Sensors Applications Symposium (SAS), 2021, pp. 67–71.
[20] S. Bajaj, A. Goyal, and D. Thakur, “Machine Learning for Blood Group Classification Based on Antigen Pattern Imaging,” IEEE International Conference on Machine Learning (ICML), pp. 456–460, 2021.
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