Animal Skin Disease Identification Using Machine Learning Methods

Abstract

Skin diseases in animals are a growing concern in veterinary medicine, particularly in rural and underserved areas that lack access to specialized care services. With the rapid advancements in computer vision and artificial intelligence techniques, novel methods have emerged to facilitate the early and accurate diagnosis of these pathological conditions. This research proposes a predictive approach based on deep learning techniques, utilizing Convolutional Neural Networks (CNNs) and multispectral imaging to detect and classify skin diseases in dogs. The work is divided into three main axes: the first axis covers the fundamentals of animal skin diseases, their clinical importance, and traditional diagnostic

methods. The second axis discusses the role of Transfer Learning and the use of state-of-the- art neural network architectures in medical image classification. The third axis details the

experimental protocol, which includes the use of multispectral images, the class_weight technique to address data imbalance, and the evaluation of several CNN models such as Xception, EfficientNet, and DenseNet121. The results demonstrated that the Xception model outperformed the other models, achieving an accuracy of 98.9%, an Area Under the Curve (AUC) of 99.7%, and a Matthews Correlation Coefficient (MCC) of 98.4%. These findings highlight the efficacy of combining multispectral imaging with deep learning techniques in veterinary diagnostics and underscore the significant potential of AI-powere d tools in enhancing animal health, especially in resource-limited regions.