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.