Abstract: Identification of blood groups plays a vital role in the medical field for any treatment, ensuring compatibility in transfusions and other procedures. Currently, this task is typically performed manually in laboratories, a time-consuming process that requires skilled experts. To address the constraints and limitations of conventional blood group prediction methods, MATLAB techniques have been developed. These techniques incorporate image processing, including segmentation processes, to classify blood groups efficiently. By collecting blood samples and processing them through image classification with feature extraction, the variety of blood types based on ABO and Rh systems can be accurately identified. This advanced methodology mitigates the drawbacks associated with traditional methods, primarily reducing manual errors and enhancing speed and accuracy. The implementation of these techniques allows for the rapid and precise classification of blood groups, which is crucial in medical emergencies and routine diagnostics. The integration of image processing with artificial intelligence significantly enhances the reliability and efficiency of blood group determination. This technological advancement not only ensures faster results but also reduces the dependency on human expertise, minimizing the risk of errors. Consequently, this innovative approach represents a significant improvement over conventional methods, providing a robust solution for the timely and accurate identification of blood groups. This development is particularly beneficial in critical situations where time is of the essence, ensuring that patients receive the correct blood type promptly, ultimately saving lives and improving healthcare outcomes.
Keywords: Blood group type, feature extraction, Histogram, Image processing, MATLAB, Segmentation.
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DOI:
10.17148/IJARCCE.2024.13715
