• Nelapati koteswaramma
• Sowmya Devarakonda
• Shruthi Sunnam
• Akanksha Pillalamari
• Krushika Chinni

Blood group detection is a critical step in medical diagnostics, blood transfusions, and surgeries, requiring rapid and accurate identification. Traditional methods for blood typing are labor-intensive and require specialized laboratory equipment and trained personnel, which can be challenging in remote areas or emergency situations. This study explores an automated, efficient, and cost-effective approach for blood group detection using image processing techniques. By capturing images of blood samples treated with specific reagents (anti-A, anti-B, anti-D, etc.), we analyze the agglutination patterns to classify blood types. The method employs preprocessing steps, including color normalization, contrast enhancement, and noise reduction, followed by feature extraction techniques to identify distinct agglutination reactions. are then applied to classify the blood group (A, B, AB, O) and Rh factor (positive or negative). Preliminary results indicate a high degree of accuracy, making this approach viable for real-time applications. This research could pave the way for portable and automated blood group detection systems, providing a quick and reliable alternative to traditional methods, particularly in resource-limited settings.

blood samples, morphological technique, segmentation, threshold, feature extraction

"Automated Blood Group Detection using Advanced Image Processing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.j456-j460, March-2025, Available :http://www.jetir.org/papers/JETIR2503960.pdf

"Automated Blood Group Detection using Advanced Image Processing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppj456-j460, March-2025, Available at : http://www.jetir.org/papers/JETIR2503960.pdf