Abstract

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.