Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

CR Maturana, AD De Oliveira, S Nadal… - Frontiers in …, 2022 - frontiersin.org
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 …

Machine learning and its applications for protozoal pathogens and protozoal infectious diseases

RS Hu, AEL Hesham, Q Zou - Frontiers in Cellular and Infection …, 2022 - frontiersin.org
In recent years, massive attention has been attracted to the development and application of
machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for …

Integrating artificial intelligence into haematology training and practice: Opportunities, threats and proposed solutions

SY Chai, A Hayat, GT Flaherty - British Journal of Haematology, 2022 - Wiley Online Library
There remains a limited emphasis on the use beyond the research domain of artificial
intelligence (AI) in haematology and it does not feature significantly in postgraduate medical …

Morphology classification of malaria infected red blood cells using deep learning techniques

FA Muhammad, R Sudirman, NA Zakaria… - … Signal Processing and …, 2025 - Elsevier
Malaria is an endemic disease that causes great harm to children and pregnant women.
Without early and proper diagnosis, it leads to organ failure, coma and eventually death. The …

Automatic patient-level recognition of four Plasmodium species on thin blood smear by a real-time detection transformer (RT-DETR) object detection algorithm: a …

E Guemas, B Routier… - Microbiology …, 2024 - Am Soc Microbiol
Malaria remains a global health problem, with 247 million cases and 619,000 deaths in
2021. Diagnosis of Plasmodium species is important for administering the appropriate …

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 …

Potent Virustatic polymer–lipid Nanomimics block viral entry and inhibit malaria parasites in vivo

A Najer, J Blight, CB Ducker, M Gasbarri… - ACS Central …, 2022 - ACS Publications
Infectious diseases continue to pose a substantial burden on global populations, requiring
innovative broad-spectrum prophylactic and treatment alternatives. Here, we have designed …

[HTML][HTML] Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review

F Grignaffini, P Simeoni, A Alisi, F Frezza - Electronics, 2024 - mdpi.com
Malaria is a disease that affects millions of people worldwide with a consistent mortality rate.
The light microscope examination is the gold standard for detecting infection by malaria …

Application of deep learning in clinical settings for detecting and classifying malaria parasites in thin blood smears

G Wang, G Luo, H Lian, L Chen… - Open forum infectious …, 2023 - academic.oup.com
Background Scarcity of annotated image data sets of thin blood smears makes expert-level
differentiation among Plasmodium species challenging. Here, we aimed to establish a deep …

Reducing data dimension boosts neural network-based stage-specific malaria detection

K Preißinger, M Kellermayer, BG Vértessy… - Scientific Reports, 2022 - nature.com
Although malaria has been known for more than 4 thousand years, it still imposes a global
burden with approx. 240 million annual cases. Improvement in diagnostic techniques is a …