Automated Diagnosis of Intestinal Parasites: A new hybrid approach and its benefits

TL;DR

This paper introduces a hybrid automated system combining fast handcrafted feature-based classification and deep neural networks for diagnosing intestinal parasites, improving accuracy while maintaining efficiency suitable for clinical use.

Contribution

The novel hybrid approach leverages complementary decision systems and misclassification probability learning to enhance diagnostic accuracy in parasite detection.

Findings

Achieved over 94% Cohen's Kappa for helminth eggs

Attained approximately 88% Cohen's Kappa for helminth larvae

Reached about 93% Cohen's Kappa for protozoa cysts

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: ($DS_1$) a simpler system based on very fast handcrafted image feature extraction and support vector machine classification and ($DS_2$) a more complex system based on a deep neural network, Vgg-16, for image feature extraction and classification. $DS_1$ is much faster than $DS_2$, but it is less accurate than $DS_2$. Fortunately, the errors of $DS_1$ are not the same of $DS_2$. During training, we use a validation set to learn the probabilities of misclassification by $DS_1$ on each class based on its confidence values. When $DS_1$ 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 $DS_2$. 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.