Plasmodium Detection Using Simple CNN and Clustered GLCM Features
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.