Background Machine learning (ML) allows the analysis of complex and large data sets and
has the potential to improve health care. The clinical microbiology laboratory, at the interface …

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 …

Background Manual microscopic examination of Leishman/Giemsa stained thin and thick
blood smear is still the “gold standard” for malaria diagnosis. One of the drawbacks of this …

Abstract Background Microscopic examination of Giemsa-stained blood films remains the
reference standard for malaria parasite detection and quantification, but is undermined by …

Background Manual microscopy remains a widely-used tool for malaria diagnosis and
clinical studies, but it has inconsistent quality in the field due to variability in training and field …

Digital pathology based on a whole slide imaging system is about to permit a major
breakthrough in automated diagnosis for rapid and highly sensitive disease detection. High …

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 …

In the domain of medical image analysis, deep learning models are heralding a revolution,
especially in detecting complex and nuanced features characteristic of diseases like tumors …

Malaria, transmitted by female Anopheles mosquitoes, is caused by the apicomplexan
parasite of the genus Plasmodium of which five infect humans, namely Plasmodium …

Introduction Light microscopy remains a standard method for detection of malaria parasites
in clinical cases but training to expert level requires considerable time. Moreover, excessive …