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Accurate detection of canine dermatological and ophthalmological diseases is essential for improving animal health and supporting veterinary diagnostics. This study introduces a comprehensive approach that integrates the YOLOv11 object detection model with advanced data augmentation and feature extraction techniques, including Autoencoder (AE), CutMix, and Local Binary Patterns (LBP). YOLOv11 provides precise localization and classification of lesions, while Autoencoders enhance feature representation by reducing noise and improving generalization. CutMix increases data diversity, aiding in model robustness, and LBP captures critical texture details relevant to skin and eye conditions. Twelve datasets were created, combining these techniques in various configurations to assess their individual and synergistic effects on model performance. The proposed method achieves a mean Average Precision at 50% Intersection over Union (mAP@50) of 97.2% on the original image dataset and 86.6% on complex images when using a hybrid dataset that integrates original images with LBP, CutMix, and Autoencoder, significantly outperforming models trained on unaugmented data. This result demonstrates the effectiveness of combining deep learning with texture analysis and advanced augmentation strategies in handling complex veterinary diagnostic tasks. The findings highlight the potential of automated systems to improve early detection and accurate diagnosis of canine diseases, offering a reliable, scalable solution for real-world clinical applications.

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Multimodal Approach for Canine Dermatological and Ophthalmological Disease Diagnosis Using YOLOv11 with Data Augmentation and Autoencoder Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Thi Diem Huong Nguyen,
  • Van Loc An Ho,
  • Vinh Dinh Nguyen

摘要

Accurate detection of canine dermatological and ophthalmological diseases is essential for improving animal health and supporting veterinary diagnostics. This study introduces a comprehensive approach that integrates the YOLOv11 object detection model with advanced data augmentation and feature extraction techniques, including Autoencoder (AE), CutMix, and Local Binary Patterns (LBP). YOLOv11 provides precise localization and classification of lesions, while Autoencoders enhance feature representation by reducing noise and improving generalization. CutMix increases data diversity, aiding in model robustness, and LBP captures critical texture details relevant to skin and eye conditions. Twelve datasets were created, combining these techniques in various configurations to assess their individual and synergistic effects on model performance. The proposed method achieves a mean Average Precision at 50% Intersection over Union (mAP@50) of 97.2% on the original image dataset and 86.6% on complex images when using a hybrid dataset that integrates original images with LBP, CutMix, and Autoencoder, significantly outperforming models trained on unaugmented data. This result demonstrates the effectiveness of combining deep learning with texture analysis and advanced augmentation strategies in handling complex veterinary diagnostic tasks. The findings highlight the potential of automated systems to improve early detection and accurate diagnosis of canine diseases, offering a reliable, scalable solution for real-world clinical applications.