Integration of Mathematical Operators Based on Decision Boundary Complexity and Combinatorial Optimization for Improved Deep Learning Classifiers

Abstract

The proliferation of complex diseases in livestock, such as lumpy skin disease, demands advanced diagnostic tools that can accurately classify and predict outbreaks. This study explores the integration of complex mathematical operators within deep learning classifiers to enhance their accuracy and efficiency in diagnosing lumpy skin disease. By focusing on the decision boundary complexity, which delineates different disease states in high-dimensional spaces, and employing combinatorial optimization techniques, we develop a novel framework that significantly improves classification performance. The methodology hinges on optimizing the configuration and combination of mathematical operators, such as gradient operators and higher-order derivatives, to refine feature extraction processes. This approach allows for a more nuanced understanding of the disease features that are critical for accurate classification. Using a dataset comprised of clinical and image data from infected cattle, our enhanced classifiers demonstrate a marked improvement in predictive accuracy compared to traditional deep learning models. The case study not only underscores the potential of integrating advanced mathematical concepts into deep learning but also sets a precedent for tackling similar challenges in veterinary medicine and beyond.