Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and Its Smartphone Based Application

Fuhad, K. M. Faizullah; Tuba, Jannat Ferdousey; Sarker, Md. Rabiul Ali; Momen, Sifat; Mohammed, Nabeel; Rahman, Tanzilur

doi:10.3390/diagnostics10050329

Cited by 123 publications

(60 citation statements)

References 62 publications

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“…Moreover, our pipeline could be supplemented by asking annotators to order cells as part of the labelling process as this has been shown to improve consensus among annotators in fluorescent imaging data of P. falciparum (26). It should be noted that there is as yet no method of validating this ground truth; ultimately, it is defined by the experts who evaluate the slides, and this phenomenon has led groups to correct standard datasets (41). Methods will have to establish "gold standard" slide/image sets, not only for the assessment of reader competency but also to ensure a better benchmark for the design of automated methods.…”

Section: Discussionmentioning

confidence: 99%

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks

Davidson

Yahiya

Chmielewski

et al. 2021

Preprint

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Microscopic examination of blood smears remains the gold standard for diagnosis and laboratory studies with malaria. Inspection of smears is, however, a tedious manual process dependent on trained microscopists with results varying in accuracy between individuals, given the heterogeneity of parasite cell form and disagreement on nomenclature. To address this, we sought to develop an automated image analysis method that improves accuracy and standardisation of cytological smear inspection but retains the capacity for expert confirmation and archiving of images. Here we present a machine-learning method that achieves red blood cell (RBC) detection, differentiation between infected and uninfected RBCs and parasite life stage categorisation from raw, unprocessed heterogeneous images of thin blood films. The method uses a pre-trained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection that performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. A residual neural network (ResNet)-50 model applied to detect infection in segmented RBCs also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Lastly, using a regression model our method successfully recapitulates intra-erythrocytic developmental cycle (IDC) stages with accurate categorisation (ring, trophozoite, schizont), as well as differentiating asexual stages from gametocytes. To accelerate our method’s utility, we have developed a mobile-friendly web-based interface, PlasmoCount, which is capable of automated detection and staging of malaria parasites from uploaded heterogeneous input images of Giemsa-stained thin blood smears. Results gained using either laboratory or phone-based images permit rapid navigation through and review of results for quality assurance. By standardising the assessment of parasite development from microscopic blood smears, PlasmoCount markedly improves user consistency and reproducibility and thereby presents a realistic route to automating the gold standard of field-based malaria diagnosis.Significance StatementMicroscopy inspection of Giemsa-stained thin blood smears on glass slides has been used in the diagnosis of malaria and monitoring of malaria cultures in laboratory settings for >100 years. Manual evaluation is, however, time-consuming, error-prone and subjective with no currently available tool that permits reliable automated counting and archiving of Giemsa-stained images. Here, we present a machine learning method for automated detection and staging of parasite infected red cells from heterogeneous smears. Our method calculates parasitaemia and frequency data on the malaria parasite intraerythrocytic development cycle directly from raw images, standardizing smear assessment and providing reproducible and archivable results. Developed into a web tool, PlasmoCount, this method provides improved standardisation of smear inspection for malaria research and potentially field diagnosis.

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Section: Discussionmentioning

confidence: 99%

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks

Davidson

Yahiya

Chmielewski

et al. 2021

Preprint

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“…A generative DL model helped create synthetic versions of the original input using an encoder and decoder. Even with such a robust approach, their work still delivered a lightweight model that can work efficiently with mobile devices [30].…”

Section: Related Workmentioning

confidence: 99%

Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears

2021

KSII TIIS

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In this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears. The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC). We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model. We applied transfer learning to increase the initial feature sets and reduced the training time to train our model. We then fine-tuned it to work with our proposed layers and retrained the entire model to learn from our prepared dataset. The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten. Additional visualization and analysis using the Gradientweighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models. This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.

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“…In order to examine the text in online forums, Facebook uses the invention of profound learning [9], among other aspects. Google and Microsoft use each profound information for image search, as well as for figures [10]. There is deep learning framework available on all stylish reasonable telephones.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Image Classification of Malaria Parasite Using Convolutional Neural Network and ADAM Optimizer

Kumar¹

2021

TURCOMAT

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Machine learning can be a technique of nursing lysis that automatically develops an analytical model. It is a branch of synthetic intelligence that believes that systems are going to learn information, determine patterns of information and decide with degraded human intervention. Machine learning addresses the question of how computers can be constructed that improve mechanically through knowledge. It lies at the intersection of technology and statistics and at the center of artificial data and information science, one in all the quickest increasing technical fields of nowadays. Recent advances in machine learning were driven by the event of latest learning and theories also as by the constant explosion. The event of latest learning algorithms and also theory and the in-progress growth within the accessibility of on-line information also as low-priced computation crystal rectifier to recent progress within the field of machine learning. Additional evidence-based decision-making could be carried out in science, technology and trade, including healthcare, production, education and monetary modelling, enforcement and promotion, with adoption of mechanical learning techniques based on data-intensive methods. The results are also available. The infection can be a life-threatening disease. The bite of a nursing partner is often transmitted in dipterous Anopheles. In infected mosquitoes, plasmodium parasite is a gift. The parasite is discharged into your blood after you bite this dipterous insect once it bites you. Once your body is composed of the parasites, they mature into the liver. The mature parasites enter the blood for several days when red blood cells start to infect. In red blood cells, parasites increase over 48-72 hours, causing infected cells to divide. The parasites still infect red blood cells, which last 2 to 3 days in cycles. This paper is used for observation of protozoan infection with a deep learning idea.

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Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and Its Smartphone Based Application

Cited by 123 publications

References 62 publications

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks

Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears

An Efficient Image Classification of Malaria Parasite Using Convolutional Neural Network and ADAM Optimizer

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