Development of a MATLAB-Based GUI for Counting Red Blood Cells and White Blood Cells, and Classification of White Blood Cells
Keywords:
Complete Blood Count (CBC), MATLAB, Red Blood Cells (RBC), White Blood Cells (WBC), WBC Subtype Classification, Image Processing, Graphical User Interface (GUI).Abstract
The Complete Blood Count (CBC) is an essential diagnostic procedure widely employed in clinical laboratories to evaluate overall health and detect conditions such as infections, anemia, and hematologic malignancies. Traditional CBC methods, including manual counting with a hemocytometer and automated analyzers, are either labor-intensive or cost-prohibitive for low-resource settings. This study presents a MATLAB-based diagnostic application designed to detect and classify red blood cells (RBCs) and white blood cells (WBCs), including WBC subtypes, from blood smear images. The system utilizes pre-annotated bounding boxes for cell localization, eliminating the need for complex image segmentation. A user-friendly graphical user interface (GUI) was developed using MATLAB App Designer, allowing real-time display of cell counts and classifications. Clinical validation with licensed medical technologists ensured the morphological accuracy of WBC subtype labels. The system achieved a WBC classification accuracy of 76.92%, an RBC count accuracy of 23.08% (38.46% within a ±2 tolerance), and 100% accuracy in WBC counting. The results demonstrate the tool's effectiveness and practicality for hematological diagnostics in academic and low-resource healthcare environments. Future work includes integrating deep learning techniques for automated classification and batch processing.
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