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 …
Opportunities and challenges: Classification of skin disease based on deep learning
B Zhang, X Zhou, Y Luo, H Zhang, H Yang… - Chinese Journal of …, 2021 - Springer
Deep learning has become an extremely popular method in recent years, and can be a
powerful tool in complex, prior-knowledge-required areas, especially in the field of …
powerful tool in complex, prior-knowledge-required areas, especially in the field of …
Deep learning for smartphone-based malaria parasite detection in thick blood smears
Objective: This work investigates the possibility of automated malaria parasite detection in
thick blood smears with smartphones. Methods: We have developed the first deep learning …
thick blood smears with smartphones. Methods: We have developed the first deep learning …
Leveraging deep learning techniques for malaria parasite detection using mobile application
Malaria is a contagious disease that affects millions of lives every year. Traditional diagnosis
of malaria in laboratory requires an experienced person and careful inspection to …
of malaria in laboratory requires an experienced person and careful inspection to …
A novel stacked CNN for malarial parasite detection in thin blood smear images
Malaria refers to a contagious mosquito-borne disease caused by parasite genus
plasmodium transmitted by mosquito female Anopheles. As infected mosquito bites a …
plasmodium transmitted by mosquito female Anopheles. As infected mosquito bites a …
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 …
Deep learning and machine learning for Malaria detection: overview, challenges and future directions
Public health initiatives must be made using evidence-based decision-making to have the
greatest impact. Machine learning algorithms are created to gather, store, process, and …
greatest impact. Machine learning algorithms are created to gather, store, process, and …
Automated microscopy for routine malaria diagnosis: a field comparison on Giemsa-stained blood films in Peru
K Torres, CM Bachman, CB Delahunt… - Malaria journal, 2018 - Springer
Abstract Background Microscopic examination of Giemsa-stained blood films remains a
major form of diagnosis in malaria case management, and is a reference standard for …
major form of diagnosis in malaria case management, and is a reference standard for …
Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images
We propose a new framework, PlasmodiumVF-Net, to analyze thick smear microscopy
images for a malaria diagnosis on both image and patient-level. Our framework detects …
images for a malaria diagnosis on both image and patient-level. Our framework detects …
Improving malaria parasite detection from red blood cell using deep convolutional neural networks
Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is
considered endemic in many parts of the world. This article focuses on improving malaria …
considered endemic in many parts of the world. This article focuses on improving malaria …