Automatic identification of Malaria using image processing and artificial neural network

Document Type

Journal Article




School of Science




Originally published as: Kanojia, M., Gandhi, N., Armstrong, L. J., & Pednekar, P. (2017). Automatic identification of Malaria using image processing and artificial neural network. In International Conference on Intelligent Systems Design and Applications (pp. 846-857). Original article available here


Malaria is a mosquito-borne infectious disease, which is diagnosed by visual microscopic assessment of Giemsa stained blood smears. Manual detection of malaria is very time consuming and inefficient. The automation of the detection of malarial cells would be very beneficial in the treatment of patients. This paper investigates the possibility of developing automatic malarial diagnosis process through the development of a Graphical User Interface (GUI) based detection system. The detection system carries out segmentation of red blood cells (RBC) and creates a database of these RBC sample images. The GUI based system extracts features from smear image which were used to execute a segmentation method for a particular blood smear image. The segmentation technique proposed in this paper is based on the processing of a threshold binary image. Watershed threshold transformation was used as a principal method to separate cell compounds. The approach described in this study was found to give satisfactory results for smear images with various qualitative characteristics. Some problems were noted with the segmentation process with some smear images showing over or under segmentation of cells. The paper also describes the feature extraction technique that was used to determine the important features from the RBC smear images. These features were used to differentiate between malaria infected and normal red blood cells. A set of features were proposed based on shape, intensity, contrast and texture. These features were used for input to a neural network for identification. The results from the study concluded that some features could be successfully used for the malaria detection.