AIMC Topic: Plasmodium
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Computer Viewing Model for Classification of Erythrocytes Infected with spp. Applied to Malaria Diagnosis Using Optical Microscope.
Medicina (Kaunas, Lithuania)
May 21, 2025
Malaria is a disease that can result in a variety of complications. Diagnosis is carried out by an optical microscope and depends on operator experience. The use of artificial intelligence to identify morphological patterns in erythrocytes would imp...
Deep learning method for malaria parasite evaluation from microscopic blood smear.
Artificial intelligence in medicine
Mar 15, 2025
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...
Improving Malaria diagnosis through interpretable customized CNNs architectures.
Scientific reports
Feb 22, 2025
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...
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
Jan 26, 2025
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...
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
Dec 31, 2024
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...
Image cropping for malaria parasite detection on heterogeneous data.
Journal of microbiological methods
Aug 20, 2024
Malaria is a deadly disease of significant concern for the international community. It is an infectious disease caused by a Plasmodium spp. parasite and transmitted by the bite of an infected female Anopheles mosquito. The parasite multiplies in the ...
MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery.
European journal of medicinal chemistry
Aug 16, 2024
Malaria remains a significant global health challenge due to the growing drug resistance of Plasmodium parasites and the failure to block transmission within human host. While machine learning (ML) and deep learning (DL) methods have shown promise in...
Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future.
Photodiagnosis and photodynamic therapy
Nov 12, 2022
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 Plasmodium falciparum and transmitted by the female anopheles mosquito. Accor...
Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images.
Sensors (Basel, Switzerland)
Jun 8, 2022
Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim's blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even de...
A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset.
Tissue & cell
Dec 31, 2020
Malaria, one of the leading causes of death in underdeveloped countries, is primarily diagnosed using microscopy. Computer-aided diagnosis of malaria is a challenging task owing to the fine-grained variability in the appearance of some uninfected and...
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