Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural...

Area of Science:

• Hematology
• Medical Imaging
• Computational Biology

Background:

• White blood cells (WBCs) are crucial for immune defense, with specific types (neutrophils, eosinophils, basophils, monocytes, lymphocytes) performing distinct functions.
• Accurate quantification of WBCs via complete blood count (CBC) tests is vital for health monitoring.
• Traditional methods can be time-consuming; deep learning offers potential for faster, accurate classification of blood cells from images.

Purpose of the Study:

• To evaluate the performance of various deep learning models, particularly Convolutional Neural Network (CNN) architectures, for classifying white blood cell types from blood film images.
• To compare model efficacy based on key performance metrics including accuracy, F1-score, recall, and precision.
• To investigate the impact of advanced optimization techniques on model performance.

Main Methods:

• Exploitation of state-of-the-art deep learning models and CNN variations.
• Comparative analysis of model performance using metrics like accuracy, F1-score, recall, and precision.
• Application of optimization techniques including normalization, mixed-up augmentation, and label smoothing to enhance a selected model.

Main Results:

• DenseNet161 demonstrated superior performance compared to other evaluated deep learning models.
• The study provides a quantitative comparison of different CNN architectures for WBC classification.
• Optimization techniques further improved the performance of the DenseNet161 model.

Conclusions:

• Deep learning, specifically CNNs like DenseNet161, offers a highly accurate and efficient method for classifying white blood cells from images.
• Advanced optimization strategies can further enhance the diagnostic capabilities of these automated systems.
• This approach has the potential to significantly improve the speed and accuracy of routine hematological analyses.