An Efficient Algorithm on Based GLCM-PNN to Diagnose Malaria

Malaria is a serious infectious disease, and early and accurate diagnosis is necessary in order to keep it under control. In this paper, we propose an efficient algorithm to diagnose malaria using Gray-Level Co-Occurrence Matrix (GLCM) and a probabilistic neural network (PNN). In the proposed algorithm, after pre-processing, the red blood cells were separated from images using an active contour model. Consequently, 44 features were extracted from the images using GLCM. Finally, the features were classified into normal and abnormal groups by PNN.The results show that compared to previous studies, the proposed algorithm led to improved results and accurately assessed 557.99 of 851 hospital records.

An Efficient Algorithm on Based GLCM-PNN to Diagnose Malaria Keywords:

malaria , probabilistic neural network , Gray-Level Co-Occurrence Matrix , active contour models , classification , Feature Extraction