Automated Blood Group Detection Using Image Processing and CNN Classification

The proposed project introduces an automatic system for blood group identification using image processing and deep learning concepts. The system starts with acquiring the input image of the blood sample. The image is then transformed into a grayscale image for better processing. A median filter will be applied to the image to eliminate noise and improve image clarity. The enhanced image will then go through a K-means clustering process, which will help segment the regions of interest of blood. From the enhanced image, features will be derived related to unique image patterns of blood groups. The blood group features will then be classified using a CNN for precise identification of blood groups. The combination of clustering concept-based image processing and CNN concept-based classification will help enhance the efficiency of blood group identification. The system will offer a dependable, contactless, and efficient way of blood group identification, which can be applied effectively in a biomedical context