The utility of serial blood film testing for the diagnosis of malaria

Makhija, Karan S.; Maloney, Samuel; Norton, Robert

doi:10.1097/pat.0000000000000190

Cited by 22 publications

(14 citation statements)

References 13 publications

(15 reference statements)

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“…In recent time's emergent amount of studies profound to the application of computer vision and machine learning technologies to the automated diagnosis of malaria. Recently allied effort [1][2][3], an automated analysis method was presented in [4] for detection and enactment of red blood cells infected by the malaria parasite.To organise RBCs, three different types of ML algorithms were established for prediction accuracy and promptness as RBCs classifiers.…”

Section: Classification Of Malaria Cellsmentioning

confidence: 99%

“…Comprehensively, an projected 3.4 billion people in 92 countries are at risk of being infected with malaria and mounting disease and 1.1 billion are at high risk. A distinct and confined area found in a currently or earlier malarious area that contains the epidemiologic and ecological factors crucial for malaria diffusion.The WHO boosts the progress of rapid and economical diagnostic tests that allow for the identification of proper treatment methods [1] [2] [3]. Malaria is a fatal disease caused by parasites that spread to people through the bites of infected female Anopheles mosquitoes.…”

Section: Introductionmentioning

confidence: 99%

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Microscopic peripheral malarial parasite detection and classification in blood smears using Gabor Filters and Machine learning algorithms

Aravinda¹,

Meng²,

R³

et al. 2021

Preprint

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Background: Malarial fever disease mainly caused by Plasmodium para-site that is infectious to red blood cells. Manual mode of blood cell counting is a tedious process, this leads to distressing method for diagnosis. This process’s mainly impacted on larger screening process.Introduction: The advanced stage of technology, computer aided detection and analysis of this malarial disease, based on Gabor Filters followed by the comparison of XG-Boost classiﬁer, Support Vector Machine and Neural Net-work Classiﬁer algorithms chosen as architecture of choice for recognition and classiﬁcation of these malarial blood cells.Objective: The goal of this paper is to slow down the complexity in model discrepancy’s, and bring it to more desirable robustness and generalization, through the model development which detects and classify the parasitized and uninfected blood cells in the given sample. Roughly 13750 parasitized and 13750 unparasitized samples was taken for experiments.Results: From the experiments the models such as S.V.M achieved 94% and XG-Boost achieved 90% neural network classiﬁer achieved 80% , out of these S.V.M performed good results in classifying and recognizing the parasitized and uninfected blood cells to increase the accuracy in decision making. Conclusion: The accomplishment of these M.L models, pretends these problems with less variance and obtained excellent results.

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Section: Classification Of Malaria Cellsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microscopic peripheral malarial parasite detection and classification in blood smears using Gabor Filters and Machine learning algorithms

Aravinda¹,

Meng²,

R³

et al. 2021

Preprint

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“…According to the 2018 World Health Organization (WHO) malaria report [1], about 219 million malaria cases were detected worldwide in 2017, causing approximately 435,000 deaths. Microscopy examination of stained thick and thin blood smears is the gold standard for malaria diagnosis [2], [3]. Microscopy examination is low-cost and is widely available, but is timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears

Yang

Poostchi

et al. 2020

IEEE J. Biomed. Health Inform.

158

103

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This work investigates the possibility of automated malaria parasite detection in thick blood smears with smartphones. Methods: We have developed the first deep learning method that can detect malaria parasites in thick blood smear images and can run on smartphones. Our method consists of two processing steps. First, we apply an intensity-based Iterative Global Minimum Screening (IGMS), which performs a fast screening of a thick smear image to find parasite candidates. Then, a customized Convolutional Neural Network (CNN) classifies each candidate as either parasite or background. Together with this paper, we make a dataset of 1819 thick smear images from 150 patients publicly available to the research community. We used this dataset to train and test our deep learning method, as described in this paper. Results: A patient-level five-fold cross-evaluation demonstrates the effectiveness of the customized CNN model in discriminating between positive (parasitic) and negative image patches in terms of the following performance indicators: accuracy (93.46% ± 0.32%), AUC (98.39% ± 0.18%), sensitivity (92.59% ± 1.27%), specificity (94.33% ± 1.25%), precision (94.25% ± 1.13%), and negative predictive value (92.74% ± 1.09%). High correlation coefficients (>0.98) between automatically detected parasites and ground truth, on both image level and patient level, demonstrate the practicality of our method. Conclusion: Promising results

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“…To identify the malarial parasite, numerous techniques have been proposed and microscopic examination of Giemsa stain blood smear is manifest [4]. Other techniques include polymerase chain reaction and rapid diagnostic tests to detect the antigen in the blood.…”

Section: Introductionmentioning

confidence: 99%

A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images

et al. 2020

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Malaria refers to a contagious mosquito-borne disease caused by parasite genus plasmodium transmitted by mosquito female Anopheles. As infected mosquito bites a person, the parasite multiplies in the host's liver and start destroying the red-cells. The disease is examined visually under the microscope for infected red-cells. This diagnosis depends upon the expertise and experience of pathologists and reports may vary in different laboratories doing a manual examination. Another way around, many machine learning techniques have been applied for spontaneous detection of blood smears. However, feature engineering is a challenging task that requires expertise to adjust positional and morphological features. Therefore, this study proposes a novel Stacked Convolutional Neural Network architecture that improves the automatic detection of malaria without considering the hand-crafted features. The 5-fold cross-validation process on 27, 558 cell images with equal instances of parasitized and uninfected cells on a publicly available dataset from the National Institute of health, the accuracy of our proposed model is 99.98%. Furthermore, the statistical results revealed that the proposed model is superior to the state-of-the-art models with 100% precision, 99.9% recall, and 99% f1-measure. INDEX TERMS Convolutional neural network (CNN), Malaria, blood smear images, deep learning, diagnostic approach.

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The utility of serial blood film testing for the diagnosis of malaria

Cited by 22 publications

References 13 publications

Microscopic peripheral malarial parasite detection and classification in blood smears using Gabor Filters and Machine learning algorithms

Microscopic peripheral malarial parasite detection and classification in blood smears using Gabor Filters and Machine learning algorithms

Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears

A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images

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