Deep Learning Approach for the Classification of Caprine Parasites

A parasitic infection is, by far and away, the most common element in animal infectious diseases whose origins may be traced back to most places worldwide. The term "parasite" refers to any organism that lives within another living thing. Because of the near closeness of the people and the animals, there is a danger that the sanitary conditions of the animals will not be adequately treated, which has the potential to have severe ramifications for the health of the people. The surrounding environment has become contaminated due to the spread of parasites and bacteria that dwell in the digestive systems of a wide variety of different animals into the surrounding environment. This has resulted in the contamination of the ecosystem. The main objective of this paper is to employ numerous methods of deep learning to identify the types of parasites found in faeces samples taken from caprine animals. The caprine animals served as the source for the later analyzed samples. Images of parasites such as Amphistome, Ascaris, B-Coli, Moniezia, Schistosoma spindale, Strongyle, and Trichuris are included in the database. These images train and evaluate several models using deep learning techniques such as YOLOv4, YOLOv5, SSD and CNN. The classification results from the study indicate that the YOLOv4 technique can diagnose infections caused by caprine parasites more promptly. This was determined by looking at the results of the performed classification. The fact that the approach produced categorization results provides support for this hypothesis.