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

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

URL: http://arxiv.org/abs/2305.01663v1
Date: Tue, 2 May 2023 10:28:07 GMT
Title: A Novel Deep Learning based Model for Erythrocytes Classification and Quantification in Sickle Cell Disease
Authors: Manish Bhatia, Balram Meena, Vipin Kumar Rathi, Prayag Tiwari, Amit Kumar Jaiswal, Shagaf M Ansari, Ajay Kumar, Pekka Marttinen
Abstract summary: We proposed a customized deep convolutional neural network (CNN) model to classify and quantify the distorted and normal morphology of erythrocytes. 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.
Score: 16.351002089332994
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.