Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data

TL;DR

This paper presents a hybrid deep learning and machine learning approach that significantly improves erythroblast detection accuracy in blood smear images, especially with limited training data, outperforming traditional models.

Contribution

It introduces a ResNet-50 based hybrid classifier that maintains high accuracy with minimal data, advancing blood cell classification in resource-limited settings.

Findings

ResNet50-SVM outperforms other models in accuracy.

High erythroblast detection precision with limited data.

Effective classification with just 1% of dataset.

Abstract

Hematological disorders, which involve a variety of malignant conditions and genetic diseases affecting blood formation, present significant diagnostic challenges. One such major challenge in clinical settings is differentiating Erythroblast from WBCs. Our approach evaluates the efficacy of various machine learning (ML) classifiers$\unicode{x2014}$SVM, XG-Boost, KNN, and Random Forest$\unicode{x2014}$using the ResNet-50 deep learning model as a backbone in detecting and differentiating erythroblast blood smear images across training splits of different sizes. Our findings indicate that the ResNet50-SVM classifier consistently surpasses other models' overall test accuracy and erythroblast detection accuracy, maintaining high performance even with minimal training data. Even when trained on just 1% (168 images per class for eight classes) of the complete dataset, ML classifiers such as SVM achieved a test accuracy of 86.75% and an erythroblast precision of 98.9%, compared to 82.03% and 98.6% of pre-trained ResNet-50 models without any classifiers. When limited data is available, the proposed approach outperforms traditional deep learning models, thereby offering a solution for achieving higher classification accuracy for small and unique datasets, especially in resource-scarce settings.