ABSTRACT
Intestinal parasites are responsible for several diseases in human beings. In order to eliminate the error-prone visual analysis of optical microscopy slides, we have investigated automated, fast, and low-cost systems for the diagnosis of human intestinal parasites. In this work, we present a hybrid approach that combines the opinion of two decision-making systems with complementary properties: (DS1) a simpler system based on very fast handcrafted image feature extraction and support vector machine classification and (DS2) a more complex system based on a deep neural network, Vgg-16, for image feature extraction and classification. DS1 is much faster than DS2, but it is less accurate than DS2. Fortunately, the errors of DS1 are not the same of DS2. During training, we use a validation set to learn the probabilities of misclassification by DS1 on each class based on its confidence values. When DS1 quickly classifies all images from a microscopy slide, the method selects a number of images with higher chances of misclassification for characterization and reclassification by DS2. Our hybrid system can improve the overall effectiveness without compromising efficiency, being suitable for the clinical routine - a strategy that might be suitable for other real applications. As demonstrated on large datasets, the proposed system can achieve, on average, 94.9%, 87.8%, and 92.5% of Cohen's Kappa on helminth eggs, helminth larvae, and protozoa cysts, respectively.
