MACHINE LEARNING SYSTEM FOR AUTOMATED BLOOD SMEAR ANALYSIS

In this paper the authors propose a decision support system for automatic blood smear analysis based on microscopic images. The images are pre-processed in order to remove irrelevant elements and to enhance the most important ones - the healthy blood cells (erythrocytes) and the pathologic (echinocytes). The separated blood cells are analyzed in terms of their most important features by the eigenfaces method. The features are the basis for designing the neural network classifier, learned to distinguish between erythrocytes and echinocytes. As the result, the proposed system is able to analyze the smear blood images in fully automatic manner and to deliver information on the number and statistics of the red blood cells, both healthy and pathologic. The system was examined on two case studies, the canine and human blood, and then confronted with the experienced medicine specialists. The accuracy of red blood cells classification into erythrocytes and echinocytes reaches 96%.