An Automatic System for Computing Malaria Parasite Density in Thin Blood Films

Oliveira, Allisson Dantas; Carvalho, Bruno M.; Prats, Clara; Espasa, Mateu; Prat, Jordi; López, Daniel; Albuquerque, Jones

doi:10.1007/978-3-319-75193-1_23

Cited by 1 publication

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“…The preparation and type of sample are important facts to consider to perform the correct identification of biological features. Table 3 summarizes the visual image differences between thick and thin blood smears and their analysis by AI techniques ( Ross et al, 2006 ; Mushabe et al, 2013 ; Dallet et al, 2014 ; Oliveira et al, 2017 ; Sankaran et al, 2017; Dantas Oliveira et al, 2018 ). Thick blood smear examination is crucial for a correct diagnosis of malaria, allowing the consequent visualization of thin blood smears for species identification ( Figure 2 ).…”

Section: Novel Diagnostic Tools By Using Image Analysis Techniquesmentioning

confidence: 99%

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

Maturana

Oliveira²,

Nadal³

et al. 2022

Front. Microbiol.

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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 common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.

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