On the public citation graph
An automatic and cost-effective parasitemia identification framework for low-end microscopy imaging devices
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s11263-013-0654-8openalex-snapshot
A Super-Resolution Framework for High-Accuracy Multiview Reconstruction
inferredcites →https://doi.org/10.1016/b978-0-12-336156-1.50061-6openalex-snapshot
Contrast Limited Adaptive Histogram Equalization
inferredcites →https://doi.org/10.1016/j.dsp.2012.10.002openalex-snapshot
Bayesian combination of sparse and non-sparse priors in image super resolution
inferredcites →https://doi.org/10.1016/j.micron.2012.11.002openalex-snapshot
Machine learning approach for automated screening of malaria parasite using light microscopic images
inferredcites →https://doi.org/10.1016/j.patcog.2005.11.014openalex-snapshot
A rule-based approach for robust clump splitting
inferredcites →https://doi.org/10.1038/nmeth.2089openalex-snapshot
NIH Image to ImageJ: 25 years of image analysis
inferredcites →https://doi.org/10.1109/34.56205openalex-snapshot
Scale-space and edge detection using anisotropic diffusion
inferredcites →https://doi.org/10.1109/embc.2013.6610346openalex-snapshot
Automated detection of malaria in Giemsa-stained thin blood smears
inferredcites →https://doi.org/10.1109/icarcv.2006.345381openalex-snapshot
Estimating Malaria Parasitaemia from Blood Smear Images
inferredcites →https://doi.org/10.1109/icip.1994.413553openalex-snapshot
Two deterministic half-quadratic regularization algorithms for computed imaging
inferredcites →https://doi.org/10.1109/ispa.2009.5297740openalex-snapshot
Bayesian Super-Resolution image reconstruction using an ℓ1 prior
inferredcites →https://doi.org/10.1109/msp.2003.1203207openalex-snapshot
Super-resolution image reconstruction: a technical overview
inferredcites →https://doi.org/10.1109/tip.2010.2080278openalex-snapshot
Variational Bayesian Super Resolution
inferredcites →https://doi.org/10.1117/12.2038844openalex-snapshot
Super-resolution restoration of motion blurred images
inferredcites →https://doi.org/10.1371/journal.pone.0061812openalex-snapshot
An Image Analysis Algorithm for Malaria Parasite Stage Classification and Viability Quantification
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.3132980openalex-extracted
Automatic Malaria Detection Technique Using ANN Classifiers
inferred← citeshttps://doi.org/10.1007/s11042-017-4495-2openalex-snapshot
A review on automated diagnosis of malaria parasite in microscopic blood smears images
inferred← citeshttps://doi.org/10.1007/s12639-019-01163-xopenalex-snapshot
Automatic detection of Plasmodium parasites from microscopic blood images
inferred← citeshttps://doi.org/10.1016/j.trsl.2017.12.004openalex-snapshot
Image analysis and machine learning for detecting malaria
inferred← citeshttps://doi.org/10.1109/jbhi.2020.3034863openalex-snapshot
Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears
inferred← citeshttps://doi.org/10.1155/2022/3626726openalex-snapshot
Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances
inferred← citeshttps://doi.org/10.1360/ssv-2021-0017openalex-snapshot
Artificial Intelligence in Malaria Diagnostics
inferred← citeshttps://doi.org/10.18196/jrc.2145openalex-snapshot
Performance assessment of Deep Learning procedures on Malaria dataset
inferred← citeshttps://doi.org/10.18280/ts.410129openalex-snapshot
Utilizing Image Analysis with Machine Learning and Deep Learning to Identify Malaria Parasites in Conventional Microscopic Blood Smear Images
inferred← citeshttps://doi.org/10.21203/rs.3.rs-11152068/v1openalex-snapshot
Segmentation Techniques for Malaria Parasites and Sickle Cells in Peripheral Blood Smear Microscopy: A Systematic Review of Artificial Intelligence Approaches for Field Deployment in Endemic Regions
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