Eimeria is a protozoan parasite that causes coccidiosis in various animal species, especially in chickens, resulting in infections characterized by intestinal damage, hemorrhagic diarrhea, lethargy, and high mortality rates in the absence of effective control measures. The rapid spread of these parasites through ingestion of food and drinking water can seriously endanger animal health and productivity, leading to significant economic losses in the chicken industry. Chicken Eimeria species are difficult to identify by conventional microscopy due to similarities in oocyst morphologies. In addition, species identification, which is significant in epidemiological studies, is a time-consuming process involving the sporulation stage and various measurements, requiring labor and expertise. Therefore, the objective of this study was to develop an automated system to classify digital micrographic images of sporulated Eimeria oocysts belonging to seven pathogenic species obtained from domestic chickens using deep transfer learning (DTL) models. This study is the first to utilize feature extraction and fine-tuning methods for classification using DTL models. In this study, 17 pre-trained DTL models were utilized for the classification process. The Xception model achieved the highest classification performance with an accuracy rate of 96.4 %, outperforming all the other models. These results highlight the efficacy of the Xception model and show that DTL models have significant potential in classifying Eimeria species. The DTL models applied in this study, which use both feature extraction and fine-tuning methods to enable species classification of sporulated oocysts of primary chicken Eimeria species, may reduce the workload of researchers in the future and can be incorporated into diagnostic tools and adapted for other practical uses in parasitology and other scientific fields.