Academic Journal
Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning
| العنوان: | Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning |
|---|---|
| المؤلفون: | Yuhang Dong, W. David Pan, Dongsheng Wu |
| المصدر: | Entropy, Vol 21, Iss 11, p 1062 (2019) |
| بيانات النشر: | MDPI AG, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Science LCC:Astrophysics LCC:Physics |
| مصطلحات موضوعية: | lossless compression, pattern classification, machine learning, malaria infection, entropy, golomb–rice codes, Science, Astrophysics, QB460-466, Physics, QC1-999 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1099-4300 |
| Relation: | https://www.mdpi.com/1099-4300/21/11/1062; https://doaj.org/toc/1099-4300 |
| DOI: | 10.3390/e21111062 |
| URL الوصول: | https://doaj.org/article/da0f16c9056248e2b17c3f4e4afe0e1d |
| رقم الانضمام: | edsdoj.0f16c9056248e2b17c3f4e4afe0e1d |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 10994300 |
|---|---|
| DOI: | 10.3390/e21111062 |