Senthilkumar Chamirti, C Sindhu, Vadivu G, Neethirajan Suresh
Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur 603203, India.
Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Kattankulathur 603203, India.
Vet Sci. 2024 Oct 17;11(10):510. doi: 10.3390/vetsci11100510.
Lumpy Skin Disease (LSD) poses a significant threat to agricultural economies, particularly in livestock-dependent countries like India, due to its high transmission rate leading to severe morbidity and mortality among cattle. This underscores the urgent need for early and accurate detection to effectively manage and mitigate outbreaks. Leveraging advancements in computer vision and artificial intelligence, our research develops an automated system for LSD detection in cattle using deep learning techniques. We utilized two publicly available datasets comprising images of healthy cattle and those with LSD, including additional images of cattle affected by other diseases to enhance specificity and ensure the model detects LSD specifically rather than general illness signs. Our methodology involved preprocessing the images, applying data augmentation, and balancing the datasets to improve model generalizability. We evaluated over ten pretrained deep learning models-Xception, VGG16, VGG19, ResNet152V2, InceptionV3, MobileNetV2, DenseNet201, NASNetMobile, NASNetLarge, and EfficientNetV2S-using transfer learning. The models were rigorously trained and tested under diverse conditions, with performance assessed using metrics such as accuracy, sensitivity, specificity, precision, F1-score, and AUC-ROC. Notably, VGG16 and MobileNetV2 emerged as the most effective, achieving accuracies of 96.07% and 96.39%, sensitivities of 93.75% and 98.57%, and specificities of 97.14% and 94.59%, respectively. Our study critically highlights the strengths and limitations of each model, demonstrating that while high accuracy is achievable, sensitivity and specificity are crucial for clinical applicability. By meticulously detailing the performance characteristics and including images of cattle with other diseases, we ensured the robustness and reliability of the models. This comprehensive comparative analysis enriches our understanding of deep learning applications in veterinary diagnostics and makes a substantial contribution to the field of automated disease recognition in livestock farming. Our findings suggest that adopting such AI-driven diagnostic tools can enhance the early detection and control of LSD, ultimately benefiting animal health and the agricultural economy.
结节性皮肤病（LSD）对农业经济构成重大威胁，特别是在像印度这样依赖牲畜的国家，因为其高传播率导致牛群中严重的发病率和死亡率。这突出了早期准确检测以有效管理和减轻疫情爆发的迫切需求。利用计算机视觉和人工智能的进步，我们的研究开发了一种使用深度学习技术的牛结节性皮肤病自动检测系统。我们使用了两个公开可用的数据集，包括健康牛和患有结节性皮肤病的牛的图像，以及受其他疾病影响的牛的额外图像，以提高特异性并确保模型专门检测结节性皮肤病而非一般疾病迹象。我们的方法包括对图像进行预处理、应用数据增强以及平衡数据集以提高模型的通用性。我们使用迁移学习评估了十多个预训练的深度学习模型——Xception、VGG16、VGG19、ResNet152V2、InceptionV3、MobileNetV2、DenseNet201、NASNetMobile、NASNetLarge和EfficientNetV2S。这些模型在不同条件下经过严格训练和测试，使用准确率、灵敏度、特异性、精确率、F1分数和AUC-ROC等指标评估性能。值得注意的是，VGG16和MobileNetV2表现最为有效，准确率分别达到96.07%和96.39%，灵敏度分别为93.75%和98.57%，特异性分别为97.14%和94.59%。我们的研究批判性地突出了每个模型 的优势和局限性，表明虽然可以实现高精度，但灵敏度和特异性对于临床适用性至关重要。通过精心详细描述性能特征并纳入患有其他疾病的牛的图像，我们确保了模型的稳健性和可靠性。这种全面的比较分析丰富了我们对深度学习在兽医诊断中的应用的理解，并为畜牧养殖中的自动疾病识别领域做出了重大贡献。我们的研究结果表明，采用这种人工智能驱动的诊断工具可以加强结节性皮肤病的早期检测和控制，最终有利于动物健康和农业经济。
