用深度学习同时识别牛的牛痘病和口蹄疫，准确率达98.2%
Simultaneous Detection of LSD and FMD in Cattle Using Ensemble Deep Learning
- 融合三种深度模型，加权平均提升多病联合诊断能力
- 在1.05万张图像上实现98.2%准确率与99.5%的AUC-ROC
- 适合基层兽医快速筛查，助力资源匮乏地区疾病防控
牛痘病（LSD）和口蹄疫（FMD）是影响牛群的高传染性病毒性疾病，导致重大经济损失和动物福利问题。两者临床症状高度重叠，且与蚊虫叮咬或化学灼伤等良性病变难以区分，影响及时防控。本研究基于来自印度、巴西和美国18个农场的10,516张专家标注图像，提出一种集成深度学习框架，融合VGG16、ResNet50与InceptionV3，并采用优化加权平均策略，实现对LSD和FMD的同步检测。模型达到98.2%的准确率，宏平均精确率98.2%、召回率98.1%、F1分数98.1%，AUC-ROC达99.5%。该方法有效解决多病症状重叠难题，支持早期、精准、自动化诊断，具备提升畜牧业管理效率、推动全球农业可持续发展的潜力，并可部署于资源有限地区。
原文摘要 · Abstract (English)
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.
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