Adaptive Hybrid Optimizer based Framework for Lumpy Skin Disease Identification

2026-01-05

arXiv.org

AI Summary

This study addresses the urgent need for early and accurate detection of Lumpy Skin Disease (LSD), a rapidly spreading viral infection threatening livestock health and global food security. To this end, the authors propose LUMPNet, an end-to-end hybrid deep learning framework that integrates YOLOv11 for precise lesion localization and an EfficientNet architecture with compound scaling for robust LSD classification. A novel adaptive hybrid optimizer is also introduced to accelerate convergence and enhance training stability. Evaluated on a public dataset, LUMPNet achieves 99% training accuracy and 98% validation accuracy, significantly outperforming existing approaches. The proposed method thus offers a highly efficient and accurate solution for the early diagnosis of LSD, with strong potential for real-world deployment in veterinary diagnostics and disease surveillance systems.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Lumpy Skin Disease
• early detection
• skin nodule identification
• livestock health
• disease outbreak prevention

Innovation

• LUMPNet
• adaptive hybrid optimizer
• YOLOv11
• EfficientNet
• Lumpy Skin Disease detection