Computer-Aided Diagnosis of Malaria Parsite using Patern Recognition Methods

In many cases of paraitic identification by visual inspection is difficult, time consuming and depends heavily on the experience of microscopists. Computer-aided diagnosis can make a significant help in saving the time, reducing workforces and the possible operator errors. The aim of this study was to assess the performance of four classifiers for detection of malaria parasite was investigated. Subjects and Methods:A total of 400 images of malaria parasite-infected blood slides were used.Intially by masking the red blood cells, in order to match the stained extracted elements, only red blood cells were used for next stage of the study. Then, the color histogram, granulometry, texture, saturation level histogram, gradient and flat­ texture features were extracted. For discriminating parasitic images from non-parasitic images four classifiers have been used: K-Nearest Neighbors (KNN), Nearest Mean (NM), 1-Nearest Neighbors (1NN), and Fisher linear discriminator (Fisher). Results: The best classification accuracy of 92.5%, which was achieved by KNN classifier. The accuracies of 1-NN, Fisher and NM classifiers were 90.25%, 85%, and 60.25%, respectively. Download paper here

Recommended citation: Malihi. L., Ansari-Asl. K., Behbahani. A.. (2015). “Computer-Aided Diagnosis of Malaria Parsite using Patern Recognition Methods.” Ajums Journals. 1(1).