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[HTML][HTML] Image analysis and machine learning for detecting malaria
Malaria remains a major burden on global health, with roughly 200 million cases worldwide
and more than 400,000 deaths per year. Besides biomedical research and political efforts …
and more than 400,000 deaths per year. Besides biomedical research and political efforts …
The development of malaria diagnostic techniques: a review of the approaches with focus on dielectrophoretic and magnetophoretic methods
The large number of deaths caused by malaria each year has increased interest in the
development of effective malaria diagnoses. At the early-stage of infection, patients show …
development of effective malaria diagnoses. At the early-stage of infection, patients show …
[HTML][HTML] Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application
Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is
detected by trained microscopists who analyze microscopic blood smear images. Modern …
detected by trained microscopists who analyze microscopic blood smear images. Modern …
Efficient deep learning-based approach for malaria detection using red blood cell smears
Malaria is an extremely malignant disease and is caused by the bites of infected female
mosquitoes. This disease is not only infectious among humans, but among animals as well …
mosquitoes. This disease is not only infectious among humans, but among animals as well …
Malaria parasite detection from peripheral blood smear images using deep belief networks
In this paper, we propose a novel method to identify the presence of malaria parasites in
human peripheral blood smear images using a deep belief network (DBN). This paper …
human peripheral blood smear images using a deep belief network (DBN). This paper …
Convolutional neural network‐based malaria diagnosis from focus stack of blood smear images acquired using custom‐built slide scanner
The present paper introduces a focus stacking‐based approach for automated quantitative
detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom …
detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom …
Parasite detection and identification for automated thin blood film malaria diagnosis
This paper investigates automated detection and identification of malaria parasites in
images of Giemsa-stained thin blood film specimens. The Giemsa stain highlights not only …
images of Giemsa-stained thin blood film specimens. The Giemsa stain highlights not only …
Point-of-care pathogen testing using photonic crystals and machine vision for diagnosis of urinary tract infections
H Liu, Z Li, R Shen, Z Li, Y Yang, Q Yuan - Nano letters, 2021 - ACS Publications
Urinary tract infections (UTIs) caused by bacterial invasion can lead to life-threatening
complications, posing a significant health threat to more than 150 million people worldwide …
complications, posing a significant health threat to more than 150 million people worldwide …
[PDF][PDF] A deep learning model for malaria disease detection and analysis using deep convolutional neural networks
Malaria is a very infectious disease that is caused by female anopheles mosquito. This
disease not only harms humans but also animals. If this disease not diagnosed properly in …
disease not only harms humans but also animals. If this disease not diagnosed properly in …
A Malaria Diagnostic Tool Based on Computer Vision Screening and Visualization of Plasmodium falciparum Candidate Areas in Digitized Blood Smears
Introduction Microscopy is the gold standard for diagnosis of malaria, however, manual
evaluation of blood films is highly dependent on skilled personnel in a time-consuming, error …
evaluation of blood films is highly dependent on skilled personnel in a time-consuming, error …