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- W3106596620 abstract "With the advancement of technology, its involvement in medical science is inevitable. Rapid progress has been made in the fields of automated disease detection and diagnosis. The same concept has been extended to the field of malaria cell analysis in this research. From the image of a cell, this model is capable of determining whether it is uninfected or parasitized with malaria infection. The method discussed here is lightweighted and easily implementable in terms of time and space complexity. Also, it has a training accuracy of 97.98% and a validation accuracy of 97.6%. A new method called “Information Padding” has been used and discussed in the methodology section." @default.
- W3106596620 created "2020-12-07" @default.
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- W3106596620 date "2020-11-24" @default.
- W3106596620 modified "2023-10-16" @default.
- W3106596620 title "Automated Classification and Detection of Malaria Cell Using Computer Vision" @default.
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- W3106596620 doi "https://doi.org/10.1007/978-981-15-7834-2_45" @default.
- W3106596620 hasPublicationYear "2020" @default.
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