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| Autores principales: | , , , , , , |
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| Formato: | Recurso digital |
| Lenguaje: | |
| Publicado: |
Zenodo
2026
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| Materias: | |
| Acceso en línea: | https://doi.org/10.5281/zenodo.19287534 |
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- Caprines have severe disease issues of gastrointestinal parasitic infections. They cause low productivity and immense economic losses on farm animals. The manual analysis of the microscope is a time consuming and expert based diagnosis which used to be traditionally employed in diagnosis. To counter such difficulties, in this paper, we provide a structure of the automatic process of detecting and classifying gastrointestinal parasites by deep learning method with the help of microscopic pictures. It involves the use of convolutional neural network termed Inception V3 that is already trained and capable of transfer learning to extract the significant features that would help categorize the types of the found parasites accurately. The images are resized and converted to RGB and normalized before they are fed into the model. The dataset is split into training, validation and testing subsets to provide a reliable and unbiased assessment of the model's performance. Experimental results show that the proposed model has high classification accuracy and low inference time. Furthermore, the trained model is embedded into a web-based application created with Streamlit, which makes it possible to predict parasites in real-time. The developed system is an efficient and easy-to-use solution to the problem of automated veterinary diagnosis.