Abstract

Background:
Lumpy Skin Disease (LSD) is an infectious cattle disease that poses a serious threat to animal welfare and the livestock industry. Conventional diagnostic approaches rely heavily on clinical examinations, laboratory testing, and veterinary expertise, making the detection process costly, labor-intensive, and time-consuming.

Aim:
This study aims to develop an automated framework for the early identification of LSD in cattle using histogram-based image enhancement, Gabor wavelet feature extraction, and deep learning techniques.

Methods:
Initially, histogram enhancement techniques were applied to cattle images to improve image quality and emphasize lesion regions. Subsequently, Gabor wavelet features with different scales and orientations were extracted to capture significant texture patterns related to skin abnormalities and disease characteristics. The extracted features were then classified into healthy and infected categories using various deep learning architectures, including CNN, DenseNet121, ResNet50V2, InceptionV3, VGG16, VGG19, and Xception. The proposed framework was evaluated using three benchmark datasets: Mendeley, Kaggle, and Veterinary Research LSD datasets.

Results:
The experimental evaluation demonstrated that the proposed framework achieved high classification accuracies of 97.5% on the Mendeley dataset, 97% on the Kaggle dataset, and 97% on the Veterinary Research LSD dataset. The integration of histogram enhancement and Gabor texture analysis with CNN-based models significantly improved detection performance compared to existing state-of-the-art methods.

Conclusion:
The proposed automated LSD identification framework provides a reliable, scalable, and cost-effective solution for early disease detection in cattle. The integration of image enhancement, texture feature extraction, and deep learning techniques can effectively support veterinarians and livestock farmers in disease monitoring and management.

Key words: Gabor Wavelet; Histogram; Convolution Neural network; DenseNet121; ResNet50V2.