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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17696701 |
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| _version_ | 1866901247847563264 |
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| author | Rushikesh Pawar, Nihar Gundale, Ashwanth Nair, Om Khamkar, Tanmay Kulkarni, Ajit Mali |
| author_facet | Rushikesh Pawar, Nihar Gundale, Ashwanth Nair, Om Khamkar, Tanmay Kulkarni, Ajit Mali |
| contents | <div>The report examines the evolution of disease diagnosis in veterinary medicine through advanced machine learning approaches. Traditional diagnostic methods are limited in handling diverse species and noisy clinical environments. To address this, the study focuses on image-based diagnostics, particularly using Convolutional Neural Networks (CNNs) [5] and unsupervised anomaly detection, which achieved 89% precision in veterinary radiographs. It also highlights the role of federated learning in enabling collaborative diagnostic models while preserving data privacy. Additional techniques such as symptom pattern recognition and clinical data clustering enhance contextual analysis. Comparative experiments on benchmark datasets [6] confirm performance improvements. The proposed system is modular, scalable, and adaptable for rural veterinary settings, with continuous learning capabilities to track evolving disease patterns. Integrated features include visual dashboards, alert systems, and clinical logging to support veterinarians in decision-making and compliance. The report concludes by suggesting future directions such as hybrid models and real-time deployment strategies for robust animal disease diagnosis in complex ecosystems.</div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17696701 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Animal Disease Diagnosis Using ML Rushikesh Pawar, Nihar Gundale, Ashwanth Nair, Om Khamkar, Tanmay Kulkarni, Ajit Mali <div>The report examines the evolution of disease diagnosis in veterinary medicine through advanced machine learning approaches. Traditional diagnostic methods are limited in handling diverse species and noisy clinical environments. To address this, the study focuses on image-based diagnostics, particularly using Convolutional Neural Networks (CNNs) [5] and unsupervised anomaly detection, which achieved 89% precision in veterinary radiographs. It also highlights the role of federated learning in enabling collaborative diagnostic models while preserving data privacy. Additional techniques such as symptom pattern recognition and clinical data clustering enhance contextual analysis. Comparative experiments on benchmark datasets [6] confirm performance improvements. The proposed system is modular, scalable, and adaptable for rural veterinary settings, with continuous learning capabilities to track evolving disease patterns. Integrated features include visual dashboards, alert systems, and clinical logging to support veterinarians in decision-making and compliance. The report concludes by suggesting future directions such as hybrid models and real-time deployment strategies for robust animal disease diagnosis in complex ecosystems.</div> |
| title | Animal Disease Diagnosis Using ML |
| url | https://doi.org/10.5281/zenodo.17696701 |