Classification of lymphoma cell image based on improved SVM

Abstract

Due to the diversity of lymphoma, its classification must rely on experienced pathologist in clinic pathologic analysis. In order to improve the accuracy of lymphoma classification, lots of image processing technologies and recognition methods were presented. The support vector machine (SVM) has been widely applied in medical image classification as an effective classification method. However, the application of SVM is blocked by the limitation that each classifier must adopt the same feature vector. In this paper, an improved SVM is proposed to overcome this limitation. Through the analysis of features of different classes, different feature vectors are obtained for each class of objects respectively. And then the improved SVM based on “one-against-one” strategy is applied to classify each class one by one. According to the results of classifying seven different lymphoma images, our classification method is effective to acquire the higher precision than conventional SVM and PSO-SVM model in lymphoma classification.