AI-Driven Solutions for Falcon Disease Classification: Concatenated ConvNeXt cum EfficientNet AI Model Approach

TL;DR

This paper presents a novel AI model combining Concatenated ConvNeXt and EfficientNet architectures to accurately classify falcon diseases, improving health monitoring in avian veterinary care.

Contribution

It introduces a concatenated AI model that outperforms traditional methods in classifying falcon diseases, advancing veterinary AI applications.

Findings

Superior accuracy over traditional methods

Effective classification of 'Normal', 'Liver', and 'Aspergillosis' cases

Demonstrated robustness across evaluation metrics

Abstract

Falconry, an ancient practice of training and hunting with falcons, emphasizes the need for vigilant health monitoring to ensure the well-being of these highly valued birds, especially during hunting activities. This research paper introduces a cutting-edge approach, which leverages the power of Concatenated ConvNeXt and EfficientNet AI models for falcon disease classification. Focused on distinguishing 'Normal,' 'Liver,' and 'Aspergillosis' cases, the study employs a comprehensive dataset for model training and evaluation, utilizing metrics such as accuracy, precision, recall, and f1-score. Through rigorous experimentation and evaluation, we demonstrate the superior performance of the concatenated AI model compared to traditional methods and standalone architectures. This novel approach contributes to accurate falcon disease classification, laying the groundwork for further advancements in avian veterinary AI applications.