Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear

This paper aims to develop the computer assisted malaria infected erythrocyte classification based on a hybrid classifier. The major issues are feature extraction, optimal feature selection and erythrocytes classification. 54 dimensional features formed by the combination of the proposed features and the existing features have been used to define the feature set. The features such as prediction error, co-occurrence of linear binary pattern, chrominance channel histogram, R-G color channel difference histogram are the newly proposed features in our system. For feature selection, the different techniques have been explored to obtain the optimal feature set. Further, the performance of the different individual classifiers (SVM, k-NN and Naive Bayes) and hybrid classifier, obtained by combining the individual classifiers, is evaluated using the optimal feature set. Using the proposed optimal feature set and hybrid model, better performances (i.e. sensitivity 95.86%, accuracy 98.5%, F-score 93.82%) have been achieved on the collected clinical database. Based on the experimental results it may be concluded that hybrid classifier provides satisfactory results with an improvement in sensitivity (1.09%, 12.04%, 0%), accuracy (0.12%, 1.15%, 1.27%) and F-score (0.7%, 5.77%, 4.61%) as compared to the individual classifiers i.e. SVM, k-NN and Naive Bayes respectively.