E-ISSN 2218-6050 | ISSN 2226-4485
 
2026, Vol: 16, Issue: 6
16 / 6Current Issue ArchiveAims and ScopeAbstracting & IndexingMost Accessed ArticlesMost Downloaded ArticlesMost Cited Articles
Required files to be uploaded

OVJ Manuscript Submissio Form
Title Page
mainarticle_template

Research Article


An enhanced deep learning approach for the automated identification of lumpy skin disease in cattle

Ithaya Rani Panneer Selvam, Saziya Tabbassum, Abdul Jainul Fathima, Selvi Shanmuga Pandian, Gul Shira Banu, Shalini Divya Prasanna Anbalagan, Hari Prasath Thinakaran.


Abstract
Background:
The rapid spread of infectious cattle diseases, particularly lumpy skin disease (LSD), has created serious challenges for animal health and livestock-based economies. Conventional diagnostic methods mainly depend on clinical examination, laboratory analysis, and expert interpretation, which are often time-consuming, expensive, and prone to subjective variations.

Aim:
This study aims to develop an automated and accurate framework for early identification of LSD in cattle by combining histogram-based image enhancement techniques with deep learning models.

Methods:
Initially, cattle skin images were pre-processed using Contrast-Limited Adaptive Histogram Equalization (CLAHE) to improve image contrast and enhance lesion visibility under uneven illumination conditions. The enhanced images were then classified using multiple deep learning architectures, including Convolutional Neural Networks, DenseNet121, ResNet50V2, InceptionV3, VGG16, VGG19, and Xception. The proposed framework was evaluated on three datasets: Mendeley, Kaggle, and an Indian veterinary research dataset.

Results:
The proposed method achieved high classification accuracies of 96.5%, 96%, and 95% on the Mendeley, Kaggle, and Indian Journal of Research datasets, respectively. Comparative analysis demonstrated that the CLAHEintegrated deep learning models outperformed several existing approaches in identifying infected and healthy cattle images.

Conclusion:
The integration of CLAHE with deep learning models provides a reliable, scalable, and cost-effective solution for automated detection of LSD in cattle. The proposed framework can support veterinarians and farmers in early disease diagnosis, improving livestock health management and clinical decision-making.

Key words: Convolutional neural network; DenseNet121; ResNet50V2; InceptionV3; VGG16.


 
ARTICLE TOOLS
Abstract
PDF Fulltext
How to cite this articleHow to cite this article
Citation Tools
Related Records
 Articles by Ithaya Rani Panneer Selvam
Articles by Saziya Tabbassum
Articles by Abdul Jainul Fathima
Articles by Selvi Shanmuga Pandian
Articles by Gul Shira Banu
Articles by Shalini Divya Prasanna Anbalagan
Articles by Hari Prasath Thinakaran
on Google
on Google Scholar


How to Cite this Article
Pubmed Style

Selvam IRP, Tabbassum S, Fathima AJ, Pandian SS, Banu GS, Anbalagan SDP, Thinakaran HP. An enhanced deep learning approach for the automated identification of lumpy skin disease in cattle. Open Vet. J.. 2026; 16(6): 3717-3730. doi:10.5455/OVJ.2026.v16.i6.44

Web Style

Selvam IRP, Tabbassum S, Fathima AJ, Pandian SS, Banu GS, Anbalagan SDP, Thinakaran HP. An enhanced deep learning approach for the automated identification of lumpy skin disease in cattle. https://www.openveterinaryjournal.com/?mno=311467 [Access: June 10, 2026]. doi:10.5455/OVJ.2026.v16.i6.44

AMA (American Medical Association) Style

Selvam IRP, Tabbassum S, Fathima AJ, Pandian SS, Banu GS, Anbalagan SDP, Thinakaran HP. An enhanced deep learning approach for the automated identification of lumpy skin disease in cattle. Open Vet. J.. 2026; 16(6): 3717-3730. doi:10.5455/OVJ.2026.v16.i6.44


Vancouver/ICMJE Style

Selvam IRP, Tabbassum S, Fathima AJ, Pandian SS, Banu GS, Anbalagan SDP, Thinakaran HP. An enhanced deep learning approach for the automated identification of lumpy skin disease in cattle. Open Vet. J.. (2026), [cited June 10, 2026]; 16(6): 3717-3730. doi:10.5455/OVJ.2026.v16.i6.44


Harvard Style

Selvam, I. R. P., Tabbassum, . S., Fathima, . A. J., Pandian, . S. S., Banu, . G. S., Anbalagan, . S. D. P. & Thinakaran, . H. P. (2026) An enhanced deep learning approach for the automated identification of lumpy skin disease in cattle. Open Vet. J., 16 (6), 3717-3730. doi:10.5455/OVJ.2026.v16.i6.44


Turabian Style

Selvam, Ithaya Rani Panneer, Saziya Tabbassum, Abdul Jainul Fathima, Selvi Shanmuga Pandian, Gul Shira Banu, Shalini Divya Prasanna Anbalagan, and Hari Prasath Thinakaran. 2026. An enhanced deep learning approach for the automated identification of lumpy skin disease in cattle. Open Veterinary Journal, 16 (6), 3717-3730. doi:10.5455/OVJ.2026.v16.i6.44


Chicago Style

Selvam, Ithaya Rani Panneer, Saziya Tabbassum, Abdul Jainul Fathima, Selvi Shanmuga Pandian, Gul Shira Banu, Shalini Divya Prasanna Anbalagan, and Hari Prasath Thinakaran. "An enhanced deep learning approach for the automated identification of lumpy skin disease in cattle." Open Veterinary Journal 16 (2026), 3717-3730. doi:10.5455/OVJ.2026.v16.i6.44


MLA (The Modern Language Association) Style

Selvam, Ithaya Rani Panneer, Saziya Tabbassum, Abdul Jainul Fathima, Selvi Shanmuga Pandian, Gul Shira Banu, Shalini Divya Prasanna Anbalagan, and Hari Prasath Thinakaran. "An enhanced deep learning approach for the automated identification of lumpy skin disease in cattle." Open Veterinary Journal 16.6 (2026), 3717-3730. Print. doi:10.5455/OVJ.2026.v16.i6.44


APA (American Psychological Association) Style

Selvam, I. R. P., Tabbassum, . S., Fathima, . A. J., Pandian, . S. S., Banu, . G. S., Anbalagan, . S. D. P. & Thinakaran, . H. P. (2026) An enhanced deep learning approach for the automated identification of lumpy skin disease in cattle. Open Veterinary Journal, 16 (6), 3717-3730. doi:10.5455/OVJ.2026.v16.i6.44


Author LoginReviewer LoginAbout PublisherOpen Access PolicyEditorial Review PolicyEditorial & Peer Review ProcessPublication Ethics and Publication Malpractice StatementConflict of Interest PolicyPlagiarism PolicyProtection of Research Participants (Statement On Human And Animal Rights)Corrections, Retractions & Expressions of ConcernDigital Archiving & Preservation PoliciesTerms of UseLicense InformationCopyright Information

About Open Veterinary Journal

Open Veterinary Journal provides high quality peer review process for original research articles, review articles, short communications and case reports i ... Read more.



For best results, please use Internet Explorer or Google Chrome.

Contact Information

All correspondence should be addressed to:

Prof. Ibrahim Eldaghayes: Editor-in-Chief

Open Veterinary Journal

Faculty of Veterinary Medicine, University of Tripoli

P.O. Box 13662, Tripoli, Libya

and


Eldaghayes Publisher

www.eldaghayes.com 

Emails: ibrahim.eldaghayes@vetmed.edu.ly

info@eldaghayes.com