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Chagas disease, caused by the parasite Trypanosoma cruzi, affects many people in Latin America. A blood test is the preferred method to generate a diagnosis of the disease; however, it is a time-consuming process, since it requires a lot of effort by experts to analyze large quantities of samples in the search of the presence of parasites. Implementation of automatic systems that facilitate the detection of the parasite in images of blood samples captured by microscope is very useful. Therefore, in this review article, the different scientific papers that use machine learning techniques to detect and segment the parasite Trypanosoma cruzi in digital images are described. [ABSTRACT FROM AUTHOR]

The objective of this paper was the development of a content-based image retrieval system, using siamese and triplet convolutional neural networks. These networks were used to generate visual descriptors, extracting semantic information from two images (siamese) or three images (triplet) at the same time. Then, a similarity learning was done, encoding these two or three visual descriptors. In the proposed scheme the storage of descriptors is not required. The experimental results show that the schemes based on convolutional neural networks extract more semantic information. The siamese and triplet architectures, apart from extracting semantic information, improved the image retrieval rate. It is concluded that the proposed scheme solved three of the main challenges in these systems, such as, semantic gap, similarity learning and storage space, which have not been solved in the previous works. [ABSTRACT FROM AUTHOR]