Chagas parasite classification in blood sample images using different machine learning architectures.

Chagas disease is a life-threatening illness mainly found in Latin America. Early identification and diagnosis of Chagas disease are critical for reducing the death rate of individuals since cures and treatments are available at the acute stage. In this work, we test and compare several deep learning classification models on smear blood sample images for the task of Chagas parasite classification. Our experiments showed that the best classification model is a deep learning architecture based on a residual network together with separable convolution blocks as feature extractors and using a support vector machine algorithm as the classifier in the final layer. This optimized model, we named Res2_SVM, with a reduced number of parameters, achieved an accuracy of [Formula: see text], precision of [Formula: see text], recall of [Formula: see text], and F1-score of [Formula: see text] on our test dataset, overcoming other machine learning models.

Effective residual convolutional neural network for Chagas disease parasite segmentation.

Considered a neglected tropical pathology, Chagas disease is responsible for thousands of deaths per year and it is caused by the parasite Trypanosoma cruzi. Since many infected people can remain asymptomatic, a fast diagnosis is necessary for proper intervention. Parasite microscopic observation in blood samples is the gold standard method to diagnose Chagas disease in its initial phase; however, this is a time-consuming procedure, requires expert intervention, and there is currently no efficient method to automatically perform this task. Therefore, we propose an efficient residual convolutional neural network, named Res2Unet, to perform a semantic segmentation of Trypanosoma cruzi parasites, with an active contour loss and improved residual connections, whose design is based on Heun's method for solving ordinary differential equations. The model was trained on a dataset of 626 blood sample images and tested on a dataset of 207 images. Validation experiments report that our model achieved a Dice coefficient score of 0.84, a precision value of 0.85, and a recall value of 0.82, outperforming current state-of-the-art methods. Since Chagas disease is a severe and silent illness, our computational model may benefit health care providers to give a prompt diagnose for this worldwide affection.

Chagas parasite detection in blood images using AdaBoost.

The Chagas disease is a potentially life-threatening illness caused by the protozoan parasite, Trypanosoma cruzi. Visual detection of such parasite through microscopic inspection is a tedious and time-consuming task. In this paper, we provide an AdaBoost learning solution to the task of Chagas parasite detection in blood images. We give details of the algorithm and our experimental setup. With this method, we get 100% and 93.25% of sensitivity and specificity, respectively. A ROC comparison with the method most commonly used for the detection of malaria parasites based on support vector machines (SVM) is also provided. Our experimental work shows mainly two things: (1) Chagas parasites can be detected automatically using machine learning methods with high accuracy and (2) AdaBoost + SVM provides better overall detection performance than AdaBoost or SVMs alone. Such results are the best ones known so far for the problem of automatic detection of Chagas parasites through the use of machine learning, computer vision, and image processing methods.

Total variation regularization cost function for demodulating phase discontinuities.

We introduce a method based on the minimization of a total variation regularization cost function for computing discontinuous phase maps from fringe patterns. The performance of the method is demonstrated by numerical experiments with both synthetic and real data.

An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images.

Chagas disease is a tropical parasitic disease caused by the flagellate protozoan Trypanosoma cruzi (T. cruzi) and currently affecting large portions of the Americas. One of the standard laboratory methods to determine the presence of the parasite is by direct visualization in blood smears stained with some colorant. This method is time-consuming, requires trained microscopists and is prone to human mistakes. In this article we propose a novel algorithm for the automatic detection of T. cruzi parasites, in microscope digital images obtained from peripheral blood smears treated with Wright's stain. Our algorithm achieved a sensitivity of 0.98 and specificity of 0.85 when evaluated against a dataset of 120 test images. Experimental results show the versatility of the method for parasitemia determination.

On high-order denoising models and fast algorithms for vector-valued images.

Variational techniques for gray-scale image denoising have been deeply investigated for many years; however, little research has been done for the vector-valued denoising case and the very few existent works are all based on total-variation regularization. It is known that total-variation models for denoising gray-scaled images suffer from staircasing effect and there is no reason to suggest this effect is not transported into the vector-valued models. High-order models, on the contrary, do not present staircasing. In this paper, we introduce three high-order and curvature-based denoising models for vector-valued images. Their properties are analyzed and a fast multigrid algorithm for the numerical solution is provided. AMS subject classifications: 68U10, 65F10, 65K10.