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Malaria

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Application of Machine Learning in a Rodent Malaria Model for Rapid, Accurate, and Consistent Parasite Counts.

The American journal of tropical medicine and hygiene
Rodent malaria models serve as important preclinical antimalarial and vaccine testing tools. Evaluating treatment outcomes in these models often requires manually counting parasite-infected red blood cells (iRBCs), a time-consuming process, which can...

Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria.

International journal of environmental research and public health
The gold standard diagnosis for malaria is the microscopic visualization of blood smears to identify parasites, although it is an expert-dependent technique and could trigger diagnostic errors. Artificial intelligence (AI) tools based on digital ima...

Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification.

Computers in biology and medicine
Over the past few decades, machine learning and deep learning (DL) have incredibly influenced a broader range of scientific disciplines. DL-based strategies have displayed superior performance in image processing compared to conventional standard met...

Diagnosis of infections using artificial intelligence techniques versus standard microscopy in a reference laboratory.

Journal of clinical microbiology
Diagnosing malaria using standard techniques is time-consuming. With limited staffing in many laboratories, this may lead to delays in reporting. Innovative technologies are changing the diagnostic landscape and may help alleviate staffing shortages....

Deciphering the climate-malaria nexus: A machine learning approach in rural southeastern Tanzania.

Public health
OBJECTIVES: Malaria remains a critical public health challenge, especially in regions like southeastern Tanzania. Understanding the intricate relationship between environmental factors and malaria incidence is essential for effective control and elim...

A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites in a realistic scenario.

Computers in biology and medicine
BACKGROUND: Malaria is a critical and potentially fatal disease caused by the Plasmodium parasite and is responsible for more than 600,000 deaths globally. Early and accurate detection of malaria parasites is crucial for effective treatment, yet conv...

Discrimination of inherent characteristics of susceptible and resistant strains of Anopheles gambiae by explainable artificial intelligence analysis of flight trajectories.

Scientific reports
Understanding mosquito behaviours is vital for the development of insecticide-treated nets (ITNs), which have been successfully deployed in sub-Saharan Africa to reduce disease transmission, particularly malaria. However, rising insecticide resistanc...

Improving Malaria diagnosis through interpretable customized CNNs architectures.

Scientific reports
Malaria, which is spread via female Anopheles mosquitoes and is brought on by the Plasmodium parasite, persists as a serious illness, especially in areas with a high mosquito density. Traditional detection techniques, like examining blood samples wit...

Deep learning method for malaria parasite evaluation from microscopic blood smear.

Artificial intelligence in medicine
OBJECTIVE: Malaria remains a leading cause of global morbidity and mortality, responsible for approximately 5,97,000 deaths according to World Malaria Report 2024. The study aims to systematically review current methodologies for automated analysis o...

Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models.

BMC medical informatics and decision making
BACKGROUND: Malaria, an infectious disease caused by protozoan parasites belonging to the Plasmodium genus, remains a significant public health challenge, with African regions bearing the heaviest burden. Machine learning techniques have shown great ...