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

TL;DR

This paper introduces an ensemble deep learning model that accurately detects LSD and FMD in cattle from images, overcoming symptom overlap challenges for early, automated diagnosis to improve disease management.

Contribution

It presents a novel ensemble framework combining multiple CNN architectures for simultaneous LSD and FMD detection using a large, diverse dataset.

Findings

Achieved 98.2% accuracy in disease detection

High precision and recall of over 98%

AUC-ROC of 99.5% indicating excellent performance

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.