[HTML][HTML] Image analysis and machine learning for detecting malaria

M Poostchi, K Silamut, RJ Maude, S Jaeger… - Translational …, 2018‏ - Elsevier
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 …

The development of malaria diagnostic techniques: a review of the approaches with focus on dielectrophoretic and magnetophoretic methods

S Kasetsirikul, J Buranapong, W Srituravanich… - Malaria journal, 2016‏ - Springer
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 …

[HTML][HTML] Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application

KMF Fuhad, JF Tuba, MRA Sarker, S Momen… - Diagnostics, 2020‏ - mdpi.com
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 …

Efficient deep learning-based approach for malaria detection using red blood cell smears

M Mujahid, F Rustam, R Shafique, EC Montero… - Scientific Reports, 2024‏ - nature.com
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 …

Malaria parasite detection from peripheral blood smear images using deep belief networks

D Bibin, MS Nair, P Punitha - IEEE Access, 2017‏ - ieeexplore.ieee.org
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 …

Convolutional neural network‐based malaria diagnosis from focus stack of blood smear images acquired using custom‐built slide scanner

GP Gopakumar, M Swetha, G Sai Siva… - Journal of …, 2018‏ - Wiley Online Library
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 …

Parasite detection and identification for automated thin blood film malaria diagnosis

FB Tek, AG Dempster, I Kale - Computer vision and image understanding, 2010‏ - Elsevier
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 …

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 …

[PDF][PDF] A deep learning model for malaria disease detection and analysis using deep convolutional neural networks

MK Gourisaria, S Das, R Sharma… - International Journal of …, 2020‏ - academia.edu
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 …

A Malaria Diagnostic Tool Based on Computer Vision Screening and Visualization of Plasmodium falciparum Candidate Areas in Digitized Blood Smears

N Linder, R Turkki, M Walliander, A Mårtensson… - PloS one, 2014‏ - journals.plos.org
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 …