Published Paper Details:

This project explores the application of deep learning techniques, specifically ResNet50, VGG, MobileNet, EfficientNet, and Inception V3 models, in the detection of Lumin Derm Disease in animals. Utilizing computer vision methods, this project analyze the effectiveness of these models in identifying characteristic patterns associated with the disease. The project aims to enhance diagnostic accuracy and efficiency in livestock health monitoring through the integration of diverse deep learning architectures. The outcomes of this research not only contribute to the advancement of diagnostic tools for veterinarians but also shed light on the nuanced intricacies of leveraging various deep learning models in the context of animal health. The findings hold promise for the development of enhanced surveillance systems, ultimately benefiting livestock management practices and ensuring prompt intervention in cases of LuminDerm Disease outbreaks.