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.