Recognition of peripheral blood cell images using convolutional neural networks

Acevedo, Andrea; Alférez, Santiago; Merino, Anna; Puigví, Laura; Rodellar, José

doi:10.1016/j.cmpb.2019.105020

Cited by 134 publications

(97 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“… 15 In a previous work, we successfully applied convolutional neural networks (CNNs) to automatically classify blood cell images. 16 Since CNNs are multilayered architectures able to extract complex and high-dimensional features from images, 17 they might be highly sensitive and specific for COVID-19 RL recognition.…”

Section: Introductionmentioning

confidence: 99%

Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype and prognosis value

Merino¹,

Vlagea

Molina

et al. 2020

J Clin Pathol

Self Cite

View full text Add to dashboard Cite

AimsAtypical lymphocytes circulating in blood have been reported in COVID-19 patients. This study aims to (1) analyse if patients with reactive lymphocytes (COVID-19 RL) show clinical or biological characteristics related to outcome; (2) develop an automatic system to recognise them in an objective way and (3) study their immunophenotype.MethodsClinical and laboratory findings in 36 COVID-19 patients were compared between those showing COVID-19 RL in blood (18) and those without (18). Blood samples were analysed in Advia2120i and stained with May Grünwald-Giemsa. Digital images were acquired in CellaVisionDM96. Convolutional neural networks (CNNs) were used to accurately recognise COVID-19 RL. Immunophenotypic study was performed throughflow cytometry.ResultsNeutrophils, D-dimer, procalcitonin, glomerular filtration rate and total protein values were higher in patients without COVID-19 RL (p<0.05) and four of these patients died. Haemoglobin and lymphocyte counts were higher (p<0.02) and no patients died in the group showing COVID-19 RL. COVID-19 RL showed a distinct deep blue cytoplasm with nucleus mostly in eccentric position. Through two sequential CNNs, they were automatically distinguished from normal lymphocytes and classical RL with sensitivity, specificity and overall accuracy values of 90.5%, 99.4% and 98.7%, respectively. Immunophenotypic analysis revealed COVID-19 RL are mostly activated effector memory CD4 and CD8 T cells.ConclusionWe found that COVID-19 RL are related to a better evolution and prognosis. They can be detected by morphology in the smear review, being the computerised approach proposed useful to enhance a more objective recognition. Their presence suggests an abundant production of virus-specific T cells, thus explaining the better outcome of patients showing these cells circulating in blood.

show abstract

Section: Introductionmentioning

confidence: 99%

Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype and prognosis value

Merino¹,

Vlagea

Molina

et al. 2020

J Clin Pathol

Self Cite

View full text Add to dashboard Cite

show abstract

“…During the training stage, the neural network labels the pixels of the training images based on the features extracted. After each image is labeled, the difference between the ground truth segmentation and what the network predicted is measured using a loss function [ 18 ]. For this case, the loss function was the cross-entropy function for k mutually exclusive classes, with k = 2.…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection

Delgado-Ortet

Molina

Alférez

et al. 2020

Entropy

Self Cite

View full text Add to dashboard Cite

Malaria is an endemic life-threating disease caused by the unicellular protozoan parasites of the genus Plasmodium. Confirming the presence of parasites early in all malaria cases ensures species-specific antimalarial treatment, reducing the mortality rate, and points to other illnesses in negative cases. However, the gold standard remains the light microscopy of May-Grünwald–Giemsa (MGG)-stained thin and thick peripheral blood (PB) films. This is a time-consuming procedure, dependent on a pathologist’s skills, meaning that healthcare providers may encounter difficulty in diagnosing malaria in places where it is not endemic. This work presents a novel three-stage pipeline to (1) segment erythrocytes, (2) crop and mask them, and (3) classify them into malaria infected or not. The first and third steps involved the design, training, validation and testing of a Segmentation Neural Network and a Convolutional Neural Network from scratch using a Graphic Processing Unit. Segmentation achieved a global accuracy of 93.72% over the test set and the specificity for malaria detection in red blood cells (RBCs) was 87.04%. This work shows the potential that deep learning has in the digital pathology field and opens the way for future improvements, as well as for broadening the use of the created networks.

show abstract

“…Acevedo, Andrea, et al [108] The main contribution of this research is a classification scheme based on a trained CNN to classify eight classes of blood cells circulating in microscopic blood smear image with overall classification accuracy of 96.2%.…”

Section: Pre-train Cnn Model Alexnetmentioning

confidence: 99%

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

et al. 2021

View full text Add to dashboard Cite

Recognition of peripheral blood cell images using convolutional neural networks

Cited by 134 publications

References 25 publications

Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype and prognosis value

Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype and prognosis value

A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

Contact Info

Product

Resources

About