Classification of eimeria species from digital micrographies using CNNs

This paper presents a model for the classification of the seven species of avian Eimeria, the protozoan parasite that causes avian coccidiosis. Digital micrographs dataset consists of 4485 isolated samples of the various species of oocytes (status of the Eimeria protozoon in which the internal structure is visually different in each species). The proposed solution applied a convolutional neural network architecture for the classification of the oocytes. Different experiments were developed to enhance the previous results of the literature, and with our proposal, we obtained a better average of correct classification for the seven species, reaching 90.42% of precision. Finally, with our strategy we used for the first time a CNN model over the Eimeria dataset, demonstrating that CNN is a robust technique for artificial vision problems.