Automated diagnosis of intestinal parasites: A new hybrid approach and its benefits

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.

Keywords: Automated diagnosis of intestinal parasites; Deep neural networks; Image classification; Microscopy image analysis; Support vector machines.