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Repository of Research and Investigative Information

Ahvaz Jundishapur University of Medical Sciences

Computer-Aided Diagnosis of Malaria Parsite using Patern Recognition Methods

Malihi, Leila and Ansari Asl, Karim and Behbahani, Abdolamir (2015) Computer-Aided Diagnosis of Malaria Parsite using Patern Recognition Methods. Jundishapur Scientific Medical Journal. ISSN 2252-0619

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Abstract

Background and Objectives: 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. Conclusion: Considering the performance of the proposed method, it can be used in the development of software for detecting malaria parasite. Thus, it can offer a significant help to researchers, managers and major planners to control malaria. ►Please cite this paper as: Malihi L, Ansari-Asl K, Behbahani A. Computer-Aided Diagnosis of Malaria Parsite using Patern Recognition Methods. Jundishapur Sci Med J 2015;14(1):65-74.

Item Type: Article
Keywords: Computer-aided diagnosis, Malaria, K-nearest neighbour classifier, Nearest mean classifier, Fisher linear discriminator
Subjects: Q Science > QR Microbiology
Divisions: Faculty of Medicine, Health and Life Sciences > School of Medicine
Depositing User: آقای بهرام سوسنی غریب وند
Date Deposited: 29 Apr 2018 07:07
Last Modified: 21 Aug 2018 03:56
URI: http://eprints.ajums.ac.ir/id/eprint/13004

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