Convolutional neural networks to automate the screening of malaria in low-resource countries

Zhao, Oliver S.; Kolluri, Nikhil; Anand, Anagata; Chu, Nicholas; Bhavaraju, Ravali; Ojha, Aditya; Tiku, Sandhya; Nguyen, Dat Tien; Chen, Ryan; Morales, Adriane; Valliappan, Deepti; Patel, Juhi; Nguyen, Kevin P.

doi:10.7717/peerj.9674

Cited by 29 publications

(20 citation statements)

References 32 publications

(33 reference statements)

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“…Mobile phone applications are being developed for the automatic detection of malaria parasites ( Cesario et al, 2012 ; Rosado et al, 2016 , 2017 ; Oliveira et al, 2017 ; Yu et al, 2020 ; Zhao et al, 2020 ). Smartphone image capturing is a suitable and easy alternative for the acquisition of blood smear images through the microscope lens.…”

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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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.

View full text Add to dashboard Cite

show abstract

“…Further, the historical case data, population density, and human mobility were employed to predict the number of cases and areas at risk [104]. Machine learning and deep learning methods were also used for classification of infection severity [105], diagnosis of malaria parasites determined by the analysis of blood smears [106], detection of antibody responses to vaccines [107], and the role of mosquitoes as disease vectors [108].…”

Section: Miscellaneous Diseasesmentioning

confidence: 99%

“…To detect malaria-infected red blood cells [105], [106] National Institute of Health (NIH) malaria dataset, Broad Institute malaria dataset.…”

Section: Appendixmentioning

confidence: 99%

Machine learning in handling disease outbreaks: a comprehensive review

Riswantini

Nugraheni

2022

Bulletin EEI

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The changes in the global environment have made impact on the evolution of infectious diseases, virus mutations, or new diseases which are challenging to be tackled with new technological advances. This work aims to identify and analyze previous studies on machine learning applications in handling disease outbreaks. Bibliometric analysis was conducted on 3,447 scientific articles selected from the Scopus database. Further, latent dirichlet analysis (LDA) method was applied to identify the topic hotspots in attempting to deepen the analysis. The LDA results identified twelve topic hotspots that can be classified into three themes: COVID-19 disease, miscellaneous diseases, and public opinion on disease outbreaks for discussion. The study reveals that the scientific structure of this domain is dominated by machine learning research on COVID-19 diseases and miscellaneous diseases caused by pathogens or some genetic factors. A huge amount of multimodal medical data was used by previous studies for prediction, forecasting, classification, or screening purposes to resolve many problems of diseases, including epidemiological surveillance, diagnosis, treatment, health monitoring, epidemic management, viral infection, and pathogenesis. Public opinions toward new diseases are also an interesting topic in addition to the public perceptions in response to the health protocol and policies.

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“…Then, the extra Conv and Relu layers are added to upsurge the depth of the model. Lastly, the max-pooling layer is used to condense the size of output features which are more willingly learned by a fully connected layer of 4,096 nodes [33].…”

Section: Pre-trained Modelmentioning

confidence: 99%

Leukocytes identification using augmentation and transfer learning based convolution neural network

2022

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Most haematological diseases can be diagnosed using the morphological analysis of the microscopic blood image. The basic routine of the morphological analysis can be performed using the microscopic device which requires the skills and experiences of the haematologists. An inexperienced haematologist can lead to critical human errors. Therefore, this paper aims to propose an automated classification system used to classify different types of leukocytes based on the convolution neural network (CNN) algorithm. CNN has achieved robust performance in various fields especially in medical applications. A dataset of microscopic blood cells images of the conforming tags (basophil, eosinophil, erythroblast, lymphocyte, monocyte, neutrophil, and platelet) was used to train and test the proposed algorithm. The augmentation and deep transfer approaches were used to improve and enhance the performance of the CNN algorithm. The overall accuracy of the proposed classifier was 98% with Visual Geometry Group-19 (VGG-19). The obtained accuracy was higher than the state-of-art algorithms. To conclude that using the augmentation and deep transfer approaches with VGG-19 can obtain better classification results.

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Convolutional neural networks to automate the screening of malaria in low-resource countries

Cited by 29 publications

References 32 publications

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

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

Machine learning in handling disease outbreaks: a comprehensive review

Leukocytes identification using augmentation and transfer learning based convolution neural network

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