Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review
Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is
transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most …
transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most …
Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future
Abstract Machine and deep learning techniques are prevalent in the medical discipline due
to their high level of accuracy in disease diagnosis. One such disease is malaria caused by …
to their high level of accuracy in disease diagnosis. One such disease is malaria caused by …
Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning
D Das, R Vongpromek, T Assawariyathipat… - Malaria Journal, 2022 - Springer
Abstract Background Microscopic examination of Giemsa-stained blood films remains the
reference standard for malaria parasite detection and quantification, but is undermined by …
reference standard for malaria parasite detection and quantification, but is undermined by …
Performance of a fully‐automated system on a WHO malaria microscopy evaluation slide set
MP Horning, CB Delahunt, CM Bachman, J Luchavez… - Malaria journal, 2021 - Springer
Background Manual microscopy remains a widely-used tool for malaria diagnosis and
clinical studies, but it has inconsistent quality in the field due to variability in training and field …
clinical studies, but it has inconsistent quality in the field due to variability in training and field …
ROENet: a ResNet-based output ensemble for malaria parasite classification
(1) Background: People may be infected with an insect-borne disease (malaria) through the
blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a …
blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a …
iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope
Introduction Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa,
with 247 million cases reported worldwide in 2021 according to the World Health …
with 247 million cases reported worldwide in 2021 according to the World Health …
ReRNet: A deep learning network for classifying blood cells
Aims Blood cell classification helps detect various diseases. However, the current
classification model of blood cells cannot always get great results. A network that …
classification model of blood cells cannot always get great results. A network that …
Patient-level performance evaluation of a smartphone-based malaria diagnostic application
Background Microscopic examination is commonly used for malaria diagnosis in the field.
However, the lack of well-trained microscopists in malaria-endemic areas impacted the most …
However, the lack of well-trained microscopists in malaria-endemic areas impacted the most …
Chronic lymphocytic leukemia progression diagnosis with intrinsic cellular patterns via unsupervised clustering
Simple Summary Distinguishing between chronic lymphocytic leukemia (CLL), accelerated
CLL (aCLL), and full-blown transformation to diffuse large B-cell lymphoma (Richter …
CLL (aCLL), and full-blown transformation to diffuse large B-cell lymphoma (Richter …
Efficient malaria parasite detection from diverse images of thick blood smears for cross-regional model accuracy
Y Zhong, Y Dan, Y Cai, J Lin, X Huang… - IEEE Open Journal …, 2023 - ieeexplore.ieee.org
Goal: The purpose of this work is to improve malaria diagnosis efficiency by integrating
smartphones with microscopes. This integration involves image acquisition and algorithmic …
smartphones with microscopes. This integration involves image acquisition and algorithmic …