Next Article in Journal
An Integrated Approach for Making Inference on the Number of Clusters in a Mixture Model
Next Article in Special Issue
Invariant Image-Based Currency Denomination Recognition Using Local Entropy and Range Filters
Previous Article in Journal
Related Entropy Theories Application in Condition Monitoring of Rotating Machineries
Previous Article in Special Issue
Fusing Feature Distribution Entropy with R-MAC Features in Image Retrieval
Open AccessArticle

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

1
Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA
2
Department of Mathematical Sciences, University of Alabama in Huntsville, Huntsville, AL 35899, USA
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(11), 1062; https://doi.org/10.3390/e21111062
Received: 12 October 2019 / Revised: 25 October 2019 / Accepted: 27 October 2019 / Published: 30 October 2019
(This article belongs to the Special Issue Entropy in Image Analysis II)
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 blood cell images using deep learning. Specifically, we investigate a practical approach where images are first classified before being compressed using stacked autoencoders. We provide probabilistic analysis on the impact of misclassification rates on compression performance in terms of the information-theoretic measure of entropy. We then use malaria infection image datasets to evaluate the relations between misclassification rates and actually obtainable compressed bit rates using Golomb–Rice codes. Simulation results show that the joint pattern classification/compression method provides more efficient compression than several mainstream lossless compression techniques, such as JPEG2000, JPEG-LS, CALIC, and WebP, by exploiting common features extracted by deep learning on large datasets. This study provides new insight into the interplay between classification accuracy and compression bitrates. The proposed compression method can find useful telemedicine applications where efficient storage and rapid transfer of large image datasets is desirable. View Full-Text
Keywords: lossless compression; pattern classification; machine learning; malaria infection; entropy; Golomb–Rice codes lossless compression; pattern classification; machine learning; malaria infection; entropy; Golomb–Rice codes
Show Figures

Figure 1

MDPI and ACS Style

Dong, Y.; Pan, W.D.; Wu, D. Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning. Entropy 2019, 21, 1062.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop