Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 45, Pages: 1-10
Salam Shuleenda Devi1*, Shah Alam Sheikh2 , Anuradha Talukdar3 and Rabul Hussain Laskar1
1Department of Electronics and Communication Engineering, National Institute of Technology, Silchar, Assam –788010, India; [email protected], [email protected] 2Department of Pathology, Silchar Medical College and Hospital, Silchar, Assam – 788014, India; [email protected] 3Cachar Cancer Hospital and Research Centre, Silchar, Assam – 788015, India; [email protected]
*Author for correspondence
Salam Shuleenda Devi Department of Electronics and Communication Engineering, National Institute of Technology, Silchar, Assam –788010, India; [email protected]
Objectives: This paper aims to develop a system for malaria infected erythrocyte classification based on the histogram feature set. Method: The method consist of pre-processing, segmentation, feature extraction based on the histogram of different color channels, feature selection and malaria infected erythrocyte classification using Artificial Neural Networks (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Naive Bayes. Findings: The experimental analysis of all the classifiers with the different combinations of features has been carried out on clinical database. Based on the experimental results it may be concluded that ANN provides the higher classification rate in comparison to other classifiers which provides an overall accuracy of 96.32% and F-score of 85.32% respectively. Applications: The proposed system may be used for the automatic recognition of the malaria infected erythrocytes in the thin blood smears.
Keywords: Erythrocyte, Histogram Features, Malaria, Microscopic Image, Thin Blood Smears
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