Blood Group Detection Using Fingerprint Images

Blood group detection is a crusial aspect of medical diagnostics, widely used in blood transfusions, organ transplants, and prenatal care. Traditional blood typing methods require blood samples and reagents, which can be invasive, timeconsuming, and costly. This research proposes a novel, non-invasive approach to identify blood group using fingerprint analysis. By using Cutting-edge techniques for image processing , we analyse unique fingerprint patterns and identify specific features linked to blood types. Our methodology incorporates image enhancement, Extracting data attributes and employing learning algorithms to classify blood groups accurately. We are planning to optimize and check the performance of the model on a robust dataset, achieving promising correctness and performance in blood group prediction. This technique holds promise to redefine blood typing processes, providing a rapid, cost-effective, and accessible solution for various medical applications, particularly in remote or resource-limited areas. Further improvements in accuracy through larger datasets and advanced algorithms could establish fingerprint-based blood group detection as a viable alternative to conventional methods.