A Cloud-Enabled Deep Learning Framework for Real-Time Red Blood Cell Infection Diagnosis
Keywords:
Red Blood Cell Infections, Decision Tree Classifier, Extra Trees Classifier, Convolutional Neural Networks, Automated DiagnosisAbstract
Red blood cell (RBC) infections, such as malaria and sickle cell disease, remain serious public healthconcerns, particularly in regions with limited diagnostic resources. Conventional diagnostic techniques,including manual microscopic examination and biochemical testing, are time-consuming, subjective, and reliant on expert interpretation
References
Patil, P.R.; Sable, G.S.; Anandgaonkar, G. Counting of WBCs and RBCs from blood images using gray thresholding. Int. J. Res. Eng. Technol. 2014, 3, 391–395.
Alomari, Y.M.; Abdullah, S.; Huda, S.N.; Zaharatul Azma, R.; Omar, K. Automatic detection and quantification of WBCs and RBCs using iterative structured circle detection algorithm. Comput. Math. Methods Med. 2014, 2014, 979302
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Published
2025-06-05
How to Cite
Komati Sathish. (2025). A Cloud-Enabled Deep Learning Framework for Real-Time Red Blood Cell Infection Diagnosis . Journal of Computational Analysis and Applications (JoCAAA), 34(6), 278–286. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4941
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