ARCHIVES
Original Article
Enhancing Blood Group Identification using pigeon inspired optimization: An Innovative Approach
Dr. SM Saravanakumar1
1 Assistant Professor, Department of Computer Science, PSG College of Arts and Science, Coimbatore, Tamilnadu, India.
Published Online: January-April 2026
Pages: 12-17
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501002
References
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23. Sun, G.; Cholakkal, H.; Khan, S.; Khan, F.; Shao, L. Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes. Proc. AAAI Conf. Artif. Intell. 2022, 34, 12047–12054. [Google Scholar] [CrossRef]
2. Zhu, J.; Yu, S.; Han, Z.; Tang, Y.; Wu, C. Underwater Object Recognition Using Transformable Template Matching Based on Prior Knowledge. Math. Probl. Eng. 2019, 2019, 2892975. [Google Scholar] [CrossRef]
3. Song, W.; Huang, P.; Wang, J.; Shen, Y.; Zhang, J.; Lu, Z.; Li, D.; Liu, D. Red Blood Cell Classification Based on Attention Residual Feature Pyramid Network. Front. Med. 2021, 8, 2638. [Google Scholar] [CrossRef]
4. Arnd, P.A.; Garratty, G. A Critical Review of Published Methods for Analysis of Red Cell Antigen-Antibody Reactions by Flow Cytometry, and Approaches for Resolving Problems with Red Cell Agglutination. Transfus. Med. Rev. 2010, 24, 172–194. [Google Scholar] [CrossRef] [PubMed]
5. Hertaeg, M.J.; Kesarawani, V.; McLiesh, H.; Walker, J.; Corrie, S.R.; Garnier, G. Wash-free paper diagnostics for the rapid detection of blood type antibodies. Analyst 2021, 146, 6970–6980. [Google Scholar] [CrossRef] [PubMed]
6. Ferraz, A.; Carvalho, V.; Machado, J. Determination of human blood type using image processing techniques. Measurement 2017, 97, 165–173. [Google Scholar] [CrossRef]
7. Sheng, N.; Liu, L.; Liu, H. Quantitative determination of agglutination based on the automatic hematology analyzer and the clinical significance of the erythrocyte-specific antibody. Clin. Chim. Acta 2020, 510, 21–25. [Google Scholar] [CrossRef]
8. Sivaranjin, S.; Sujatha, C.M. Deep learning-based diagnosis of Parkinson’s disease using convolutional neural network. Multimed. Tools Appl. 2020, 79, 15467–15479. [Google Scholar] [CrossRef]
9. Lu, M.; Wu, D.; Jin, Y.; Shi, J.; Xu, B.; Cong, J.; Ma, Y.; Lu, J. A Novel Gaussian Ant Colony Algorithm for Clustering Cell Tracking. Discret. Dyn. Nat. Soc. 2021, 2021, 9205604. [Google Scholar] [CrossRef]
10. Zhu, J.; Yu, S.; Gao, L.; Han, Z.; Tang, Y. Saliency-Based Diver Target Detection and Localization Method. Math. Probl. Eng. 2020, 2020, 3186834. [Google Scholar] [CrossRef]
11. Sharma, S.; Gupta, S.; Gupta, D.; Juneja, S.; Gupta, P.; Dhiman, G.; Kautish, S. Deep Learning Model for the Automatic Classification of White Blood Cells. Comput. Intell. Neurosci. 2022, 2022, 7384131. [Google Scholar] [CrossRef] [PubMed]
12. Liu, M.; Hu, L.; Tang, Y.; Wang, C.; He, Y.; Zeng, C.; Lin, K.; He, Z.; Huo, W. A Deep Learning Method for Breast Cancer Classification in the Pathology Images. IEEE J. Biomed. Health Inform. 2022, 26, 5025–5032. [Google Scholar] [CrossRef] [PubMed]
13. Shaban, S.A.; Elsheweikh, D.L. Blood Group Classification System Based on Image Processing Techniques. Intell. Autom. Soft Comput. 2022, 31, 817–834. [Google Scholar] [CrossRef]
14. Suhang, G.; Vong, C.M.; Wong, P.K.; Wang, S. Fast Training of Adversarial Deep Fuzzy Classifier by Downsizing Fuzzy Rules with Gradient Guided Learning. IEEE Trans. Fuzzy Syst. 2021, 30, 1967–1980. [Google Scholar] [CrossRef]
15. Guo, Y.; Pang, Z.; Du, J.; Jiang, F.; Hu, Q. An Improved AlexNet for Power Edge Transmission Line Anomaly Detection. IEEE Access 2020, 8, 97830–97838. [Google Scholar] [CrossRef]
16. Chang, Y.; Wang, W. A Deep Learning-Based Weld Defect Classification Method Using Radiographic Images with a Cylindrical Projection. IEEE Trans. Instrum. Meas. 2021, 70, 1–11. [Google Scholar] [CrossRef]
17. Han, X.; Zhong, Y.; Cao, L.; Zhang, L. Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification. Remote Sens. 2017, 9, 848. [Google Scholar] [CrossRef] [Green Version]
18. Dawud, A.M.; Yurtkan, K.; Oztoprak, H. Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning. Comput. Intell. Neurosci. 2019, 2019, 4629859. [Google Scholar] [CrossRef] [Green Version]
19. Niu, Z.; Zhong, G.; Yu, H. A review on the attention mechanism of deep learning. Neurocomputing 2021, 452, 48–62. [Google Scholar] [CrossRef]
20. Liu, M.; Tang, J. Audio and Video Bimodal Emotion Recognition in Social Networks Based on Improved AlexNet Network and Attention Mechanism. J. Inf. Process. Syst. 2021, 17, 754–771. [Google Scholar]
21. Li, H.; Luo, D.; Sun, Y. A Novel Method to Recognize and Classify Based on an E-nose. Measurement 2021, 183, 109809. [Google Scholar] [CrossRef]
22. Li, L.; Doroslovacki, M.; Loew, M.H. Approximating the Gradient of Cross-Entropy Loss Function. IEEE Access 2020, 8, 111626–111635. [Google Scholar] [CrossRef]
23. Sun, G.; Cholakkal, H.; Khan, S.; Khan, F.; Shao, L. Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes. Proc. AAAI Conf. Artif. Intell. 2022, 34, 12047–12054. [Google Scholar] [CrossRef]
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