Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data

• URL: http://arxiv.org/abs/2411.15592v2
• Date: Tue, 26 Nov 2024 04:09:46 GMT
• Title: Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data
• Authors: Buddhadev Goswami, Adithya B. Somaraj, Prantar Chakrabarti, Ravindra Gudi, Nirmal Punjabi,
• Abstract summary: Hematological disorders, which involve 1% of conditions and genetic diseases, present significant diagnostic challenges. Our approach evaluates various machine learning settings offering efficacy of various machine variety learning (ML) models. When data is available, the proposed solution is a solution for achieving higher accuracy for small and unique datasets.
• Score: 0.08388591755871733
• License:
• 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.

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