MALARIA DIAGNOSIS USING CNN FEATURE EXTRACTOR, PARASITE INFLATOR AND DOUBLE HIDDEN LAYER EXTREME LEARNING MACHINE ALGORITHM

Authors

  • Mrs. D.SOWMYA, RACHAMSHETTY VAISHNAVI, SATHWIKA YADAGIRI, SAMALA PRIYANKA

Keywords:

Malaria diagnosis system, CNN feature extractor, Machine Learning, Double hidden layer , Learning machine algorithm, Data Mining, Data Augmentation

Abstract

Malaria, a perilous and fatal disease transmitted through the bite of female Anopheles mosquitoes
carrying Plasmodium parasites, poses significant threats in high-risk regions like South-East Asia, the Eastern
Mediterranean, the Western Pacific, and the Americas. With approximately 400 types of Anopheles mosquitoes,

References

Malaria Microscopy Quality Assurance Manual—Version 2, World Health Org., Geneva, Switzerland, 2016.

W. R. J. Taylor, J. Hanson, G. D. H. Turner, N. J. White, and A. M. Dondorp, ‘‘Respiratory manifestations of malaria,’’ Chest, vol. 142, no. 2,

pp. 492–505, Aug. 2012.

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Published

2024-02-15

How to Cite

Mrs. D.SOWMYA, RACHAMSHETTY VAISHNAVI, SATHWIKA YADAGIRI, SAMALA PRIYANKA. (2024). MALARIA DIAGNOSIS USING CNN FEATURE EXTRACTOR, PARASITE INFLATOR AND DOUBLE HIDDEN LAYER EXTREME LEARNING MACHINE ALGORITHM. Journal of Computational Analysis and Applications (JoCAAA), 32(2), 330–338. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2707

Issue

Vol. 32 No. 2 (2024): JOCAAA

Section

Articles

License

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This work is licensed under a Creative Commons Attribution 4.0 International License.