Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks

Molina, Ángel; Rodellar, José; Boldú, Laura; Acevedo, Andrea; Alférez, Santiago; Merino, Anna

doi:10.1016/j.compbiomed.2021.104680

Cited by 29 publications

(15 citation statements)

References 35 publications

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“…34,35 However, a high number of false positives was observed. Molina et al, 36,37 using images obtained under a microscope, demonstrated the usefulness of classification models for the automatic identification of malaria parasites, differentiating them from other RBC inclusions.…”

Section: Discussionmentioning

confidence: 99%

Performance of the new MC‐80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600

Merino,

Laguna,

Rodríguez‐García

et al. 2023

Int J Lab Hematology

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IntroductionMindray MC‐80 is an automated system for digital imaging of white blood cells (WBCs) and their pre‐classification. The objective of this work is to analyse its performance comparing it with the CellaVision® DM9600.MethodsA total of 445 samples were used, 194 normal and 251 abnormal: acute leukaemia (100), myelodysplastic syndromes/myeloproliferative neoplasms (33), lymphoid neoplasms (50), plasma cell neoplasms (14), infections (49) and thrombocytopenia (5). WBC pre‐classification values with the MC‐80 and DM9600 were compared with (1) the microscope, (2) Mindray BC‐6800Plus differentials in only normal samples, and (3) confirmed or reclassified images (post‐classification). Pearson's correlation, Lin's concordance, Passing‐Bablok regression, and Bland–Altman plots were used. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values for abnormal cells using the MC‐80 were calculated.ResultsThe PPV and NPV were above 98% and 99%, for normal samples. For immature granulocytes (IG), NPV and PPV were 100% and 74.2%. When comparing the WBC differentials using the MC‐80, the microscope and the BC‐6800Plus, no differences were found except for basophils and IG. Our results showed good agreement between the pre‐ and post‐classification of normal WBC, including IG, quantified by high correlation and concordance values (0.91–1). Sensitivity and specificity for blasts were 0.984 and 0.640. The MC‐80 detected abnormal lymphocytes in 30% of the smears from patients with lymphoid neoplasm. Plasma cell identification was better using the DM9600. The sensitivity and specificity for erythroblast detection were 1 and 0.890.ConclusionWe found that the MC‐80 shows high performance for WBC differentials for both normal samples and patients with haematological diseases.

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Section: Discussionmentioning

confidence: 99%

Performance of the new MC‐80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600

Merino,

Laguna,

Rodríguez‐García

et al. 2023

Int J Lab Hematology

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“…This would also prevent any problems due to reagent shortage, machine breakdown, or computer issues. Automatic staining of blood smears combined with artificial intelligence (AI) for species identification and parasitaemia count appears to be the best partner for XN-31 in the near future [ 18 , 24 ]. Many teams or companies are working on this topic, but unfortunately, we are still far from commercial deployment of these AI machines [ 2 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of fluorescence flow-cytometry technology using Sysmex XN-31 for imported malaria in a non-endemic setting

et al. 2022

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Malaria diagnosis based on microscopy is impaired by the gradual disappearance of experienced microscopists in non-endemic areas. Aside from the conventional diagnostic methods, fluorescence flow cytometry technology using Sysmex XN-31, an automated haematology analyser, has been registered to support malaria diagnosis. The aim of this prospective, monocentric, non-interventional study was to evaluate the diagnostic accuracy of the XN-31 for the initial diagnosis or follow-up of imported malaria cases compared to the reference malaria tests including microscopy, loop mediated isothermal amplification, and rapid diagnostic tests. Over a one-year period, 357 blood samples were analysed, including 248 negative and 109 positive malaria samples. Compared to microscopy, XN-31 showed sensitivity of 100% (95% CI: 97.13–100) and specificity of 98.39% (95% CI: 95.56–100) for the initial diagnosis of imported malaria cases. Moreover, it provided accurate species identification asfalciparumor non-falciparumand parasitaemia determination in a very short time compared to other methods. We also demonstrated that XN-31 was a reliable method for patient follow-up on days 3, 7, and 28. Malaria diagnosis can be improved in non-endemic areas by the use of dedicated haematology analysers coupled with standard microscopy or other methods in development, such as artificial intelligence for blood slide reading. Given that XN-31 provided an accurate diagnosis in 1 min, it may reduce the time interval before treatment and thus improve the outcome of patient who have malaria.

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“…Images of thin blood smear were segmented into individual red blood cells. A VGG-16 pre-trained model was trained on the dataset of single red blood cell images achieving a classification accuracy of 99.5% [9]. Zhao et al, compared the performance of a 17-layer deep CNN model and a an AlexNet mode pretrained on CIFAR for the classification of malaria parasite from thin blood smear images.…”

Section: Relevant Literaturementioning

confidence: 99%

Classification of Red Blood Cell Abnormality in Thin Blood Smear Images using Convolutional Neural Networks

Muhammad,

Sudirman,

Zakaria

et al. 2023

J. Phys.: Conf. Ser.

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One of the most common morphological red blood cell abnormalities encountered during routine thin blood smear microscopy for the detection of malaria parasite is the rouleaux formation, which is the stacking together of red blood cells to form a chain. Rouleaux formation signifies an underlying infection and as such microscopists are mandated to report its presence. A lot of work has been done in automating malaria diagnosis using deep learning, but no model has been developed which is capable of detecting rouleaux formation in malaria infected red blood cells. Thus, this study collected 231 peripheral blood smear (PBS) images of normal red blood cell morphology and 231 PBS images with rouleaux morphology. The images were pre-processed and segmented into equal instances of 3044 coloured images of size 750×750 pixels. Two convolutional neural network (CNN) models were developed and trained to classify the images into normal red blood cell morphology or rouleaux morphology. The CNN models were trained on two different image sizes: 300×300 and 500×500. The first CNN model achieved validation accuracy/loss values of 87.91%/0.8177 and 56.58%/1.4090 when trained on images of sizes 300×300 and 500×500 respectively. In the second CNN model, the CNN layers of the first model were replaced with depthwise separable CNN layers, it was also trained on images of sizes 300×300 and 500×500 achieving validation accuracy/loss values of 90.95%/0.2804 and 87.75%/0.5904 respectively. This study demonstrates the capability of CNN models in detecting red blood cell morphology abnormality in thin smear images at an optimal image size of 300×300.

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Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks

Cited by 29 publications

References 35 publications

Performance of the new MC‐80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600

Performance of the new MC‐80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600

Diagnostic accuracy of fluorescence flow-cytometry technology using Sysmex XN-31 for imported malaria in a non-endemic setting

Classification of Red Blood Cell Abnormality in Thin Blood Smear Images using Convolutional Neural Networks

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