English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The shape of erythrocytes or red blood cells is altered in severalpathological conditions. Therefore, identifying and quantifying differenterythrocyte shapes can help diagnose various diseases and assist in designing atreatment strategy. Machine Learning (ML) can be efficiently used to identifyand quantify distorted erythrocyte morphologies. In this paper, we proposed acustomized deep convolutional neural network (CNN) model to classify andquantify the distorted and normal morphology of erythrocytes from the imagestaken from the blood samples of patients suffering from Sickle cell disease (SCD). We chose SCD as a model disease condition due to the presence of diverseerythrocyte morphologies in the blood samples of SCD patients. For theanalysis, we used 428 raw microscopic images of SCD blood samples and generatedthe dataset consisting of 10, 377 single-cell images. We focused on threewell-defined erythrocyte shapes, including discocytes, oval, and sickle. Weused 18 layered deep CNN architecture to identify and quantify these shapeswith 81% accuracy, outperforming other models. We also used SHAP and LIME forfurther interpretability. The proposed model can be helpful for the quick andaccurate analysis of SCD blood samples by the clinicians and help them make theright decision for better management of SCD.

A Novel Deep Learning based Model for Erythrocytes Classification and Quantification in Sickle Cell Disease