Deep Learning-Based White Blood Cell Classification Through a Free and Accessible Application

Abstract

Background Microscopy of peripheral blood smears (PBS) continues to play a fundamental role in hematology diagnostics, offering detailed morphological insights that complement automated blood counts. Examination of a stained blood film by a trained technician is among the most frequently performed tests in clinical hematology laboratories. Nevertheless, manual smear analysis is labor-intensive, time-consuming, and prone to considerable variability between observers. These challenges have spurred interest in automated, deep learning-based approaches to enhance efficiency and consistency in blood cell assessment. Methods We designed a convolutional neural network (CNN) using a ResNet-50 backbone, applying standard transfer-learning techniques for white blood cell (WBC) classification. The model was trained on a publicly available dataset of approximately 4,000 annotated peripheral smear images representing eight WBC types. The image processing workflow included automated nucleus detection, normalization, and extensive augmentation (rotation, scaling, etc.) to improve model generalization. Training was performed with the PyTorch Lightning framework for efficient development. Application The final model was integrated into a lightweight web application and deployed on Hugging Face Spaces, allowing accessible browser-based inference. The application provides an easy-to-use interface to upload images, which are then automatically cropped and analyzed in real-time. This open and free tool is intended to provide immediate classification results. It is also a useful tool for laboratory technologists without requiring specialized hardware or software. Results Testing on an independent set revealed that the ResNet-50 network reached 98.67% overall accuracy. Performance was consistently high across all eight WBC categories. Precision, recall, and specificity closely matched the overall accuracy, indicating well-balanced classification. However, for the assessment of real-world generalization, the model was tested on an external heterogeneous dataset from different sources. It performed with 86.33% accuracy, reflecting strong performance outside of its main training data. The confusion matrix showed negligible misclassifications. This suggested consistent distinction between leukocyte types. Conclusion This study indicates that a lightweight AI tool can support peripheral smear analysis by offering rapid and consistent WBC identification via a web interface. Such a system may reduce laboratory workload and observer variability, particularly in resource-limited or remote settings where expert microscopists are scarce, and serve as a practical training aid for personnel learning cell morphology. Limitations include reliance on a single dataset, which may not encompass all staining or imaging variations, and evaluation performed offline. Future work will aim to expand dataset diversity, enable real-time integration with digital microscopes, and conduct clinical validation to broaden applicability and adoption. Application link: https://huggingface.co/spaces/xDyas/wbc-classifier