  Malaria is a serious disease caused by the Plasmodium parasite that
transmitted through the bite of a female Anopheles mosquito and invades human
erythrocytes. Malaria must be recognized precisely in order to treat the
patient in time and to prevent further spread of infection. The standard
diagnostic technique using microscopic examination is inefficient, the quality
of the diagnosis depends on the quality of blood smears and experience of
microscopists in classifying and counting infected and non-infected cells.
Convolutional Neural Networks (CNN) is one of deep learning class that able to
automate feature engineering and learn effective features that could be very
effective in diagnosing malaria. This study proposes an intelligent system
based on simple CNN for detecting malaria parasites through images of thin
blood smears. The CNN model obtained high sensitivity of 97% and relatively
high PPV of 81%. This study also proposes a false positive reduction method
using feature clustering extracted from the gray level co-occurrence matrix
(GLCM) from the Region of Interests (ROIs). Adding the GLCM feature can
significantly reduce false positives. However, this technique requires manual
set up of silhouette and euclidean distance limits to ensure cluster quality,
so it does not adversely affect sensitivity.
