Vol. 3 No. 1 (2025): Issue - 1
Articles

Deep Learning-Based Identification of High-Risk Zones for Lumpy Skin Disease in Cattle

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  • G Ramasubba Reddy,
  • Shaik Jaffar Hussain,
  • Penubaka Balaji,
  • L Jayasree,
  • K Divya,
  • M Sudhakara
G Ramasubba Reddy
Department of Computer Science and Engineering, Sai Rajeswari Institute of Technology, Proddatur, Andhra Pradesh, India.
Shaik Jaffar Hussain
Department of Computer Science and Engineering, Sri Venkateswara Institute of Science and Technology, Kadapa.
Penubaka Balaji
Department of Computer Science and Engineering, K L University, Vaddeswaram, Guntur, Andhra Pradesh, India.
L Jayasree
Dept of Computer Science and Engineering, Sri Padmavati Mahila Visva Vidyalayam, Tirupati - 517 502, AP, India.
K Divya
Department of Computer Science and Engineering, Narayana Engineering College, Nellore, Andhra Pradesh.
M Sudhakara
Senior Computer Vision Scientist, ChiStats Labs Private Limited, Pune.
DOI: https://doi.org/10.5281/zenodo.15005382

Published 2025-03-11

Keywords

  • Lumpy Skin Disease,
  • Cattle Disease Diagnosis,
  • Deep Learning,
  • Convolutional Neural Networks (CNN),
  • Image Segmentation,
  • Color Histogram,
  • Extreme Learning Machine (ELM),
  • CLSD Dataset,
  • Livestock Health Monitoring
    • ...More
    Less

How to Cite

G Ramasubba Reddy, Shaik Jaffar Hussain, Penubaka Balaji, L Jayasree, K Divya, & M Sudhakara. (2025). Deep Learning-Based Identification of High-Risk Zones for Lumpy Skin Disease in Cattle. Milestone Transactions on Medical Technometrics, 3(1), 156–167. https://doi.org/10.5281/zenodo.15005382

Abstract

Lumpy skin disease (LSD) is a virus infection in cattle, mainly transmitted through mosquitoes, such as blood-eating insects. The disease creates essential challenges for the livestock industry by affecting milk and beef production and domestic and international trade. Resource limitations, lack of specialized expertise, and lack of time often obstruct traditional clinical approaches. This study introduces a deep learning-based framework for segmenting and classifying LSD-affected skin regions to address these challenges. The proposed approach leverages a 10-layer Convolutional Neural Network (CNN) trained on a curated Cattle Lumpy Skin Disease (CLSD) dataset. The significance of skin discoloration in disease identification is that a color histogram enhances feature extraction. The segmented affected regions undergo further processing through a deep pre-trained CNN for feature extraction, followed by threshold-based binarization. Finally, classification is performed using an Extreme Learning Machine (ELM) classifier, achieving an accuracy of 96% on the CLSD dataset. Comparative analysis with existing state-of-the-art techniques demonstrates the effectiveness of the proposed methodology, highlighting its potential for reliable LSD diagnosis in cattle.
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