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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.12889 |
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| _version_ | 1866911384503058432 |
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| author | Ayon, Nazibul Basar Hasib, Abdul Ahmed, Md. Faishal Rahman, Md. Sadiqur Islam, Kamrul Hasan, T. M. Mehrab Akib, A. S. M. Ahsanul Sarkar |
| author_facet | Ayon, Nazibul Basar Hasib, Abdul Ahmed, Md. Faishal Rahman, Md. Sadiqur Islam, Kamrul Hasan, T. M. Mehrab Akib, A. S. M. Ahsanul Sarkar |
| contents | Lumpy Skin Disease (LSD) and Foot-and-Mouth Disease (FMD) are highly contagious viral diseases affecting cattle, causing significant economic losses and welfare challenges. Their visual diagnosis is complicated by significant symptom overlap with each other and with benign conditions like insect bites or chemical burns, hindering timely control measures. Leveraging a comprehensive dataset of 10,516 expert-annotated images from 18 farms across India, Brazil, and the USA, this study presents a novel Ensemble Deep Learning framework integrating VGG16, ResNet50, and InceptionV3 with optimized weighted averaging for simultaneous LSD and FMD detection. The model achieves a state-of-the-art accuracy of 98.2\%, with macro-averaged precision of 98.2\%, recall of 98.1\%, F1-score of 98.1\%, and an AUC-ROC of 99.5\%. This approach uniquely addresses the critical challenge of symptom overlap in multi-disease detection, enabling early, precise, and automated diagnosis. This tool has the potential to enhance disease management, support global agricultural sustainability, and is designed for future deployment in resource-limited settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12889 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Simultaneous Detection of LSD and FMD in Cattle Using Ensemble Deep Learning Ayon, Nazibul Basar Hasib, Abdul Ahmed, Md. Faishal Rahman, Md. Sadiqur Islam, Kamrul Hasan, T. M. Mehrab Akib, A. S. M. Ahsanul Sarkar Computer Vision and Pattern Recognition Lumpy Skin Disease (LSD) and Foot-and-Mouth Disease (FMD) are highly contagious viral diseases affecting cattle, causing significant economic losses and welfare challenges. Their visual diagnosis is complicated by significant symptom overlap with each other and with benign conditions like insect bites or chemical burns, hindering timely control measures. Leveraging a comprehensive dataset of 10,516 expert-annotated images from 18 farms across India, Brazil, and the USA, this study presents a novel Ensemble Deep Learning framework integrating VGG16, ResNet50, and InceptionV3 with optimized weighted averaging for simultaneous LSD and FMD detection. The model achieves a state-of-the-art accuracy of 98.2\%, with macro-averaged precision of 98.2\%, recall of 98.1\%, F1-score of 98.1\%, and an AUC-ROC of 99.5\%. This approach uniquely addresses the critical challenge of symptom overlap in multi-disease detection, enabling early, precise, and automated diagnosis. This tool has the potential to enhance disease management, support global agricultural sustainability, and is designed for future deployment in resource-limited settings. |
| title | Simultaneous Detection of LSD and FMD in Cattle Using Ensemble Deep Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.12889 |