Automated classification of five types of ovine white blood cells using Convolutional Neural Network and image processing

Background The differential classification of white blood cells (WBCs) is crucial for diagnosing various health conditions in veterinary medicine. Traditional manual classification is time-consuming and subjective. Methods This study presents an automated system for classifying five types of ovine WBCs using image processing and Convolutional Neural Networks (CNN). Blood samples from 36 adult sheep were used to create 500 microscopic images (100 per cell type). A comprehensive image processing pipeline was implemented in MATLAB and a custom CNN architecture was designed for five-class classification. Results The CNN model achieved an overall classification accuracy of 96.72% through 5-fold cross-validation, demonstrating robust performance across all five leukocyte types. Conclusion The proposed system provides a reliable, efficient, and accurate tool for automated WBC classification in sheep, with potential applications in veterinary diagnostics.