Machine learning-based detection and quantification of red blood cells in Cholistani cattle: A pilot study

Abstract

This study presents the first account of using machine learning to detect and count normal and abnormal red blood cells (RBCs), including tear-drop cells and schistocytes, in Cholistani cattle from Pakistan. A Support Vector Machine (SVM) model was applied and compared with manual counting methods. Pre-annotated blood smear images were preprocessed using contrast stretching transformation, followed by segmentation and resizing. Labeled datasets were augmented, and Principal Component Analysis (PCA) was employed for feature reduction. The dataset was randomly split into training (80 %) and testing (20 %) subsets, and the SVM model was trained and evaluated accordingly. No statistically significant difference (P ≥ 0.05) was observed between manual and machine learning-based RBC counts for all the studied cell types. The highest classification probability was recorded for normal RBCs (87 %), followed by tear-drop cells (84 %) and schistocytes (73 %). Accuracy was highest for tear-drop cells (0.991), followed by normal RBCs (0.965) and schistocytes (0.707). Precision values followed a similar trend, with the highest for normal RBCs (0.932), followed by tear-drop cells (0.921) and schistocytes (0.855). These findings suggest that machine learning, particularly SVM-based models, can accurately and precisely detect and count normal RBCs and tear-drop cells in Cholistani cattle. However, further refinements are needed to improve RBC detection using convolution neural networks or other deep learning approaches. This study highlights the potential of artificial intelligence for hematological assessments in veterinary medicine.