Classification of plasmodium falciparum based on textural and morphological features

Malaria is a disease caused by plasmodium parasites transmitted through the bites of female anopheles-mosquito that infect human red blood cell (RBC). The standard malaria diagnosis based on manual examination thick and thin smear, which heavily depends microscopist experience. This study proposed system can identify life stages falciparum in RBC. image preprocessing process was done illumination correction using gray world assumption, contrast enhancement shadow correction, extraction saturation component, noise filtering. segmentation applied Otsuthresholding morphological operation. test results showed use artificial neural network (ANN) combination texture features gives better when compared to only or morphology features. feature achieved an accuracy 82.67%, sensitivity 82.18%, specificity 94.17%, thus improving decision-making for diagnosis.