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<p>Camels are resilient animals that play a crucial role in arid ecosystems and desert communities. However, distinguishing between visually similar camel breeds–particularly among Arabian camels–remains a challenging task. This paper introduces a novel image dataset containing 1,620 images of Arabian and Non-Arabian camels, collected and annotated by the authors. Based on this dataset, we propose an explainable multi-stream deep learning architecture for fine-grained classification using a hierarchical adaptive framework. The model operates in two stages: (1) binary classification to distinguish Arabian camels from Non-Arabian camels, and (2) multi-class classification to identify five distinct Arabian camel breeds–Homor, Majaheem, Sofor, Waddah, and Shaele. The multi-stream design enables the model to process global context, local features, and semantic cues in parallel, significantly improving feature extraction and classification accuracy. Among several evaluated CNNs–including DenseNet121, MobileNetV2, InceptionV3, and ResNet50–DenseNet121 achieved the highest performance, yielding 98% accuracy in binary classification and 76% in multi-class classification. To address class imbalance and enhance generalization, we applied online data augmentation, class-balanced focal, and the Adam optimizer. For interpretability, Grad-CAM was integrated to visualize key decision-making regions, enhancing transparency and trust in AI-based predictions. Despite challenges such as visual similarity among certain breeds and dataset imbalance, our approach demonstrates promising results and establishes a foundation for future work in automated camel breed identification and livestock management.</p>

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Explainable multi stream deep learning for fine grained camel breed classification using a Novel Arabian and Non Arabian dataset

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月18日

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  • Hany El-Ghaish,
  • Dina M. Ibrahim,
  • Amany M. Sarhan,
  • Aboul Ella Hassanien

摘要

Camels are resilient animals that play a crucial role in arid ecosystems and desert communities. However, distinguishing between visually similar camel breeds–particularly among Arabian camels–remains a challenging task. This paper introduces a novel image dataset containing 1,620 images of Arabian and Non-Arabian camels, collected and annotated by the authors. Based on this dataset, we propose an explainable multi-stream deep learning architecture for fine-grained classification using a hierarchical adaptive framework. The model operates in two stages: (1) binary classification to distinguish Arabian camels from Non-Arabian camels, and (2) multi-class classification to identify five distinct Arabian camel breeds–Homor, Majaheem, Sofor, Waddah, and Shaele. The multi-stream design enables the model to process global context, local features, and semantic cues in parallel, significantly improving feature extraction and classification accuracy. Among several evaluated CNNs–including DenseNet121, MobileNetV2, InceptionV3, and ResNet50–DenseNet121 achieved the highest performance, yielding 98% accuracy in binary classification and 76% in multi-class classification. To address class imbalance and enhance generalization, we applied online data augmentation, class-balanced focal, and the Adam optimizer. For interpretability, Grad-CAM was integrated to visualize key decision-making regions, enhancing transparency and trust in AI-based predictions. Despite challenges such as visual similarity among certain breeds and dataset imbalance, our approach demonstrates promising results and establishes a foundation for future work in automated camel breed identification and livestock management.