用拼接模型提升猎鹰疾病分类准确率
AI-Driven Solutions for Falcon Disease Classification: Concatenated ConvNeXt cum EfficientNet AI Model Approach
- 拼接ConvNeXt与EfficientNet模型进行特征融合
- 在三种疾病上达到98.7%准确率
- 适合禽类健康监测与智能兽医系统研发
养鹰是一项古老的技艺，对猎鹰的健康监控至关重要，尤其在狩猎期间。本文提出一种创新方法，结合ConvNeXt与EfficientNet两个AI模型，用于区分‘正常’、‘肝脏’和‘曲霉菌病’三类状态。研究使用全面的数据集进行训练与评估，采用准确率、精确率、召回率和F1分数作为指标。通过严格实验，证明该拼接模型性能优于传统方法及单一架构。该方法为猎鹰疾病的精准分类提供了可靠方案，推动了鸟类兽医人工智能应用的发展。
原文摘要 · Abstract (English)
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.
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