Simultaneous Detection of LSD and FMD in Cattle Using Ensemble Deep Learning

2026-01-19

AI Summary

This study addresses the diagnostic challenge posed by the high clinical similarity between lumpy skin disease (LSD) and foot-and-mouth disease (FMD), which are often confused with benign dermatological conditions, leading to delayed detection and control. To tackle this issue, the authors propose a novel ensemble deep learning framework for simultaneous multi-disease classification, integrating VGG16, ResNet50, and InceptionV3 architectures through transfer learning and an optimized weighted averaging strategy. The model was trained and validated on a dataset of 10,516 expert-annotated images, achieving 98.2% accuracy, 98.1% macro-averaged recall and F1-score, and an AUC-ROC of 99.5% in concurrent LSD and FMD recognition. These results significantly outperform existing approaches, demonstrating enhanced accuracy and practical utility for early diagnosis in field settings.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Language and Vision
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Lumpy Skin Disease
• Foot-and-Mouth Disease
• symptom overlap
• visual diagnosis
• cattle diseases

Innovation

• Ensemble Deep Learning
• Simultaneous Disease Detection
• Lumpy Skin Disease
• Foot-and-Mouth Disease
• Weighted Model Averaging