Lumpy Skin Disease (LSD) is a contagious viral infection that significantly deteriorates livestock health, thereby posing a serious threat to the global economy and food security. Owing to its rapid spread characteristics, early and precise identification is crucial to prevent outbreaks and ensure timely intervention. In this paper, we propose a hybrid deep learning-based approach called LUMPNet for the early detection of LSD. LUMPNet utilizes image data to detect and classify skin nodules -- the primary indicator of LSD. To this end, LUMPNet uses YOLOv11, EfficientNet-based CNN classifier with compound scaling, and a novel adaptive hybrid optimizer. More precisely, LUMPNet detects and localizes LSD skin nodules and lesions on cattle images. It exploits EfficientNet to classify the localized cattle images into LSD-affected or healthy categories. To stabilize and accelerate the training of YOLOv11 and EfficientNet hybrid model, a novel adaptive hybrid optimizer is proposed and utilized. We evaluate LUMPNet at various stages of LSD using a publicly available dataset. Results indicate that the proposed scheme achieves 99% LSD detection training accuracy, and outperforms existing schemes. The model also achieves validation accuracy of 98%. Moreover, for further evaluation, we conduct a case study using an optimized EfficientNet-B0 model trained with the AdamW optimizer, and compare its performance with LUMPNet. The results show that LUMPNet achieves superior performance.

翻译：块状皮肤病（LSD）是一种传染性病毒感染，会严重损害牲畜健康，从而对全球经济和粮食安全构成严重威胁。由于其快速传播的特性，早期精准识别对于预防疫情爆发和确保及时干预至关重要。本文提出了一种名为LUMPNet的混合深度学习方法，用于LSD的早期检测。LUMPNet利用图像数据检测和分类皮肤结节——这是LSD的主要指标。为此，LUMPNet采用YOLOv11、基于复合缩放EfficientNet的CNN分类器以及一种新颖的自适应混合优化器。具体而言，LUMPNet检测并定位牛只图像上的LSD皮肤结节和病变区域。它利用EfficientNet将定位后的牛只图像分类为LSD感染或健康类别。为稳定并加速YOLOv11与EfficientNet混合模型的训练，本文提出并采用了一种新型自适应混合优化器。我们使用公开数据集在LSD的不同阶段评估LUMPNet。结果表明，所提方案实现了99%的LSD检测训练准确率，且优于现有方案。该模型的验证准确率也达到98%。此外，为进一步评估，我们使用经AdamW优化器训练的优化版EfficientNet-B0模型进行案例研究，并将其性能与LUMPNet进行比较。结果显示LUMPNet具有更优性能。