[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 …

[HTML][HTML] An overview of organs-on-chips based on deep learning

J Li, J Chen, H Bai, H Wang, S Hao, Y Ding, B Peng… - Research, 2022 - spj.science.org
Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in
biomedical and chemical research and have emerged as one of the most advanced and …

Deep malaria parasite detection in thin blood smear microscopic images

A Maqsood, MS Farid, MH Khan, M Grzegorzek - Applied Sciences, 2021 - mdpi.com
Malaria is a disease activated by a type of microscopic parasite transmitted from infected
female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions …

Automated image processing method for the diagnosis and classification of malaria on thin blood smears

NE Ross, CJ Pritchard, DM Rubin, AG Duse - Medical and Biological …, 2006 - Springer
Malaria is a serious global health problem, and rapid, accurate diagnosis is required to
control the disease. An image processing algorithm to automate the diagnosis of malaria on …

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 …

[HTML][HTML] A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images

G Díaz, FA González, E Romero - Journal of biomedical informatics, 2009 - Elsevier
Visual quantification of parasitemia in thin blood films is a very tedious, subjective and time-
consuming task. This study presents an original method for quantification and classification …

Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells

HS Park, MT Rinehart, KA Walzer, JTA Chi, A Wax - PloS one, 2016 - journals.plos.org
Malaria detection through microscopic examination of stained blood smears is a diagnostic
challenge that heavily relies on the expertise of trained microscopists. This paper presents …

Computer vision for microscopy diagnosis of malaria

FB Tek, AG Dempster, I Kale - Malaria journal, 2009 - Springer
This paper reviews computer vision and image analysis studies aiming at automated
diagnosis or screening of malaria infection in microscope images of thin blood film smears …

Splitting touching cells based on concave points and ellipse fitting

X Bai, C Sun, F Zhou - Pattern recognition, 2009 - Elsevier
A new touching cells splitting algorithm based on concave points and ellipse fitting is
proposed in this paper. The algorithm includes two parts: contour pre-processing and ellipse …

Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

N KT, K Prasad, BMK Singh - Medical & biological engineering & …, 2022 - Springer
Anemia is a blood disorder which is caused due to inadequate red blood cells and
hemoglobin concentration. It occurs in all phases of life cycle but is more dominant in …