Acquisition of digital images and identification of Aedes aegypti mosquito eggs using classification and deep learning.
The mosquito Aedes aegypti can transmit some diseases, which makes the study of the proliferation of this vector a necessary task. With the use of traps made in the laboratory, called ovitraps, it is possible to map egg deposition in a community. Through a camera, coupled with a magnifying glass are acquired images containing the elements (eggs) to be counted. First, the goal is to find pixels with a similar color to mosquito eggs, for that, we take advantage of the slice color method. From these already worked images, a process of transfer learning with a convolutional neural network (CNN) is carried out. The intention is to separate which elements are actually eggs from the others. In 10\% of the test images, the count performed by the model in relation to the actual number of eggs was considered to be weakly correlated, this occurs in images that have a high density of eggs or appear black elements that resemble mosquito eggs, but they are not. For the remaining 90\% of test images, the counting was considered to be perfectly correlated.
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