Classification red blood cells using support vector machine

The shape of red blood cells (RBCs) contributes to clinical diagnoses of blood diseases. The field of medical imaging has become more important because of the increasing need for automated and efficient diagnoses within a short period of time. Imaging technique plays an important role in RBC research for hematology. Classification is an important component of the retrieval system which allows one to distinguish between normal RBCs and abnormal RBCs which indicate anemia. In this paper, image processing techniques that use the optimization segmentation and mean filter play an important role in obtaining the geometric, texture and color features related to RBC images by using a photo imaging microscope. The support vector machine, which is an advanced kernel-based technique, is used to classify RBC data as either normal or abnormal, the proposed classifier algorithm achieved very good accuracy rates with validation measure of sensitivity, specificity and Kappa to be 100%, 0.998% and 0.9944 respectively.