The shape of erythrocytes or red blood cells is altered in several
pathological conditions. Therefore, identifying and quantifying different
erythrocyte shapes can help diagnose various diseases and assist in designing a
treatment strategy. Machine Learning (ML) can be efficiently used to identify
and quantify distorted erythrocyte morphologies. In this paper, we proposed a
customized deep convolutional neural network (CNN) model to classify and
quantify the distorted and normal morphology of erythrocytes from the images
taken 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 diverse
erythrocyte morphologies in the blood samples of SCD patients. For the
analysis, we used 428 raw microscopic images of SCD blood samples and generated
the dataset consisting of 10, 377 single-cell images. We focused on three
well-defined erythrocyte shapes, including discocytes, oval, and sickle. We
used 18 layered deep CNN architecture to identify and quantify these shapes
with 81% accuracy, outperforming other models. We also used SHAP and LIME for
further interpretability. The proposed model can be helpful for the quick and
accurate analysis of SCD blood samples by the clinicians and help them make the
right decision for better management of SCD.

本文利用深度卷积神经网络来分类和量化镰状细胞贫血患者的红细胞形态，建立了包含 10,377 个细胞图像的数据集，使用 18 层神经网络架构，以 81% 的准确率鉴别出红细胞的三种不同形态，并使用了 SHAP 和 LIME 来进一步解释模型结果。