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:: Volume 17, Issue 2 (Summer 2015) ::
J Mil Med 2015, 17(2): 115-125 Back to browse issues page
Automated Malaria Diagnosis and the Plasmodium Species Recognition System
Morteza Izadi, Tooraj Abbasian *, Nematollah Jonaidi
Control & Intelligent Processing Center of Excellence, ECE School, College of Engineering, University of Tehran, Tehran, Iran , najafabadi@ut.ac.ir
Abstract:   (5518 Views)

Aims: This research has aimed to design and manufacture a smart system for malaria detection and the determination of the plasmodium type in blood samples. Moreover, the design of a low-cost motorized microscope for automated imaging of blood smears has been conducted in this project.

Methods: Image processing novel methods have been exercised to extract suitable features for the segmentation of red blood cells, malaria parasite detection, and plasmodium type recognition. Afterwards, the pattern recognition methods of artificial intelligence were used to classify and label the extracted objects. Furthermore, the combination of mechatronics and electronics contributes to the manufacturing of a microscope with the capability of moving the blood slide automatically while taking images concurrently.

Results: In this research, 12 blood samples contaminated with 4 types of malaria plasmodium were used as the input data. From these slides, 700 images were obtained and used for training and testing the proposed diagnosis algorithms. The accuracy of malaria detection and plasmodium type recognition were achieved more than 95% and 91% respectively by the proposed system.

Conclusion: The automated plasmodium type recognition system offers an accuracy almost equal to the level of human experts or even more than human experts in some cases. The low charges of this system and eliminating the need for an expert physician of malaria detection in the endemic regions are the other advantages of this system.

Keywords: Malaria, Automated Diagnosis, Image Processing, Artificial Intelligence
Full-Text [PDF 976 kb]   (3263 Downloads)    
Type of Study: Orginal Research | Subject: military medcine
Received: 2015/05/4 | Accepted: 2015/06/22 | Published: 2015/10/12
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Izadi M, Abbasian T, Jonaidi N. Automated Malaria Diagnosis and the Plasmodium Species Recognition System. J Mil Med. 2015; 17 (2) :115-125
URL: http://militarymedj.ir/article-1-1388-en.html

Volume 17, Issue 2 (Summer 2015) Back to browse issues page
مجله طب‌نظامی Journal Mil Med
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