Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning

Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning

Malaria is a severe public health problem worldwide, with some developing countries being most affected. Reliable remote diagnosis of malaria infection will benefit from efficient compression of high-resolution microscopic images. This paper addresses a lossless compression of malaria-infected red b...

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Bibliographic Details
Main Authors: Yuhang Dong, W. David Pan, Dongsheng Wu
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Entropy
Subjects:
lossless compression
pattern classification
machine learning
malaria infection
entropy
golomb–rice codes
Online Access:https://www.mdpi.com/1099-4300/21/11/1062

Internet

https://www.mdpi.com/1099-4300/21/11/1062

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