Lumpy Skin Disease Detection Using GUI Based Deep Learning Model in Cattle

Cattle are susceptible to the extremely contagious viral infection known as Lumpy Skin Disease (LSD), which results in skin lesions and substantial financial losses for the livestock industry. Visual Geometry Group 16(VGG16), Visual Geometry Group 19(VGG19), and DenseNet121 are a few examples of Convolutional Neural Networks (CNNs) that have shown success in correctly diagnosing LSD lesions. Transfer learning is frequently used to optimize previously trained models on certain datasets. Model performance is measured using evaluation measures like accuracy, precision, recall, and F1-score. In comparison to VGG16 and VGG19, CNN and DenseNet121 provide a balance between accuracy and processing economy. To implement effective control measures and lessen the financial burden on cow populations, it is important to accurately classify LSD lesions. The proposed system with DenseNet121 model enables early detection and intervention. DenseNet121 has given the highest accuracy of 99.10% with approximately balanced dataset of size 4759. The proposed model also includes an attractive user interface to display and compare results of all models for a given input image. Categorization report, a confusion matrix, and a test accuracy calculation all are obtained as results.