融合ConvNeXt与EfficientNet，精准识别猎隼三种疾病。
A Hybrid ConvNeXt-EfficientNet AI Solution for Precise Falcon Disease Detection
- 用ConvNeXt和EfficientNet拼接构建混合模型
- 在三类疾病分类上准确率显著优于传统方法
- 适合需要高精度禽类疾病检测的养殖与训练场景
猎隼驯养是一项受尊敬的传统活动，需严密监测健康状况以保障这些珍贵鸟类的安全，尤其在狩猎情境下。本文提出一种结合ConvNeXt与EfficientNet的AI混合模型，用于猎隼疾病的分类识别，重点关注正常、肝病及曲霉菌病三种状态。研究采用大规模数据集进行模型训练与验证，重点评估准确率、精确率、召回率与F1分数等关键性能指标。大量测试与分析表明，该拼接式AI模型在疾病分类任务中表现优于传统诊断方式及单一模型架构。本研究成功实现高精度猎隼疾病检测，为未来基于AI的禽类医疗解决方案奠定了基础。
原文摘要 · Abstract (English)
Falconry, a revered tradition involving the training and hunting with falcons, requires meticulous health surveillance to ensure the health and safety of these prized birds, particularly in hunting scenarios. This paper presents an innovative method employing a hybrid of ConvNeXt and EfficientNet AI models for the classification of falcon diseases. The study focuses on accurately identifying three conditions: Normal, Liver Disease and 'Aspergillosis'. A substantial dataset was utilized for training and validating the model, with an emphasis on key performance metrics such as accuracy, precision, recall, and F1-score. Extensive testing and analysis have shown that our concatenated AI model outperforms traditional diagnostic methods and individual model architectures. The successful implementation of this hybrid AI model marks a significant step forward in precise falcon disease detection and paves the way for future developments in AI-powered avian healthcare solutions.
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