Lossless compression of curated erythrocyte images using deep autoencoders for malaria infection diagnosis

Shen, Hongda; Pan, W. David; Dong, Yuhang; Alim, M. M. F.

doi:10.1109/pcs.2016.7906393

Cited by 19 publications

(14 citation statements)

References 14 publications

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“…Previously, we used autoencoders to exploit the correlations of similar images to achieve high compression on red blood cell images [ 26 ]. For this sake, two separate autoencoders were trained using images known in advance to belong to one of the two classes (either normal cells, or malaria infected cells).…”

Section: Methodsmentioning

confidence: 99%

“…Deep learning based neural networks can be trained on samples within the same class to learn the common features shared by these samples. In our prior work [ 26 ], we proposed a coding scheme for red blood cell images by using stacked autoencoders, where the reconstruction residues were entropy-coded to achieve lossless compression. Specifically, we trained two separate stacked autoencoders to automatically learn the discriminating features from input images of infected and non-infected cells.…”

Section: Introductionmentioning

confidence: 99%

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

Dong

Pan

2019

Entropy

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Dong

Pan

2019

Entropy

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“…The highest accuracy of 84.0% is achieved by a Bayesian network with the 19 most important features. Shen et al [22] used a stacked autoencoder to learn features automatically from infected and infected images of cells.…”

Section: Related Workmentioning

confidence: 99%

A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images

et al. 2020

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Malaria refers to a contagious mosquito-borne disease caused by parasite genus plasmodium transmitted by mosquito female Anopheles. As infected mosquito bites a person, the parasite multiplies in the host's liver and start destroying the red-cells. The disease is examined visually under the microscope for infected red-cells. This diagnosis depends upon the expertise and experience of pathologists and reports may vary in different laboratories doing a manual examination. Another way around, many machine learning techniques have been applied for spontaneous detection of blood smears. However, feature engineering is a challenging task that requires expertise to adjust positional and morphological features. Therefore, this study proposes a novel Stacked Convolutional Neural Network architecture that improves the automatic detection of malaria without considering the hand-crafted features. The 5-fold cross-validation process on 27, 558 cell images with equal instances of parasitized and uninfected cells on a publicly available dataset from the National Institute of health, the accuracy of our proposed model is 99.98%. Furthermore, the statistical results revealed that the proposed model is superior to the state-of-the-art models with 100% precision, 99.9% recall, and 99% f1-measure. INDEX TERMS Convolutional neural network (CNN), Malaria, blood smear images, deep learning, diagnostic approach.

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“…There are also some tasks for which algorithms are not yet set. For example for spam messages, it's too difficult to convert the input to output (Shen et al, 2016). In this age of vision, about 85% (approx.)…”

Section: Deep Learning For Computer Visionmentioning

confidence: 99%

Vision-based malaria parasite image analysis: a systematic review

Pattanaik

Swarnkar

2019

IJBRA

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Background: Malaria is one of the classic neglected serious diseases in many developing countries. The early stage of disease detection, accurate parasite count, detection of the aggressiveness of the disease, technical limitations, lack of expertise in malaria diagnosis and smart tools, lack of good quality healthcare services, funds so on are the challenges found during malaria diagnosis that requires a deeper analysis. Objectives: This paper aims to give a review of the automated diagnosis or visual inspection of malaria parasites using histology images of thin or thick blood film smears. Methods and Results: Various computer-aided diagnosis techniques are in use to solve tasks meticulously in a stratified description paradigm using non-linear transformation architectures. Conclusion: This work elaborates a comprehensive study of various computer vision diagnostic approaches already proposed in this field with a future direction for better quicker malaria identification.

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Lossless compression of curated erythrocyte images using deep autoencoders for malaria infection diagnosis

Cited by 19 publications

References 14 publications

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

A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images

Vision-based malaria parasite image analysis: a systematic review

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