Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data (2411.15592v2)

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 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.

• The paper proposes a hybrid model combining deep learning and ML classifiers for accurate erythroblast differentiation, effective with limited data.
• The ResNet50-SVM model achieved 86.75% test accuracy and 98.9% erythroblast precision with only 1% training data.
• This hybrid methodology is practical for medical diagnostics in resource-limited settings, enabling adaptable AI solutions.

Overview of Classifier Enhanced Deep Learning Model for Erythroblast Differentiation

The paper "Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data" addresses the diagnostic challenges associated with hematological disorders, particularly the differentiation of erythroblasts from WBCs. The authors propose a novel approach that leverages ML classifiers, specifically SVM, XG-Boost, KNN, and Random Forest, in conjunction with a ResNet-50-based deep learning framework to enhance the accuracy of erythroblast detection from blood smear images, even with constrained training data.

Key Contributions

1. Integration of Classifiers and Deep Learning: The paper presents a hybrid methodology combining traditional ML classifiers with deep learning via the ResNet-50 model, effectively utilizing it as a feature extractor. Among the classifiers, SVM emerged as particularly effective in enhancing performance on limited datasets.
2. Efficacy with Limited Data: A significant contribution of this work lies in its ability to maintain high test accuracies with minimal data. For training set sizes as small as 1% (168 images per class), the ResNet50-SVM model achieved a test accuracy of 86.75% and erythroblast precision of 98.9%, outperforming standalone deep learning models.
3. Comprehensive Evaluation: The study provides a thorough comparison of the proposed integrated models across multiple data splits, highlighting the ResNet50-SVM approach's superior performance in low-resource scenarios.

Numerical Results and Claims

• The authors report a standout performance of the SVM integrated model achieving 86.75% test accuracy at the lowest data split (1%), contrasting with the best standalone ResNet-50's 82.03%.
• In terms of erythroblast detection, the ResNet50-SVM model maintained high precision (98.9%) across most tested configurations, illustrating its robustness.

Implications and Future Directions

This research demonstrates the practical utility of combining feature-rich deep learning models with customizable machine learning classifiers. The methodology is particularly suited for scenarios involving limited data and resources, such as those frequently encountered in under-resourced clinical settings.

The study indicates potential pathways to wider application in AI for healthcare diagnostics, emphasizing the need for adaptable solutions that operate proficiently even with limited datasets. Future research directions may explore the integration of more advanced image segmentation techniques to pre-process blood smear images more effectively, thereby further reducing misclassification rates. Additionally, incorporating real-world data variability into training sets could enhance model robustness across diverse clinical environments.

In conclusion, this work significantly contributes to the field of AI in medical diagnostics by effectively addressing a prevalent challenge using a hybrid model approach. The successful application of sophisticated machine learning techniques in constrained data settings underscores an important advancement towards practical and scalable AI solutions in medicine.