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<p>This study presents a novel deep learning approach addressing the critical shortage of veterinary expertise in India’s North Eastern Hill (NEH) region through automated identification of parasitic infections in livestock. We developed a Convolutional Neural Network (CNN) architecture capable of analyzing both standard and microscopic images to identify and classify 16 distinct parasitic species. The model comprises four convolutional layers (32, 64, 128, 256 filters) with ReLU activation and MaxPooling for efficient feature extraction, followed by Dense layers and a Softmax classifier. The model was trained on a comprehensive dataset of over 5,334 annotated images, achieving 96% accuracy after 30 training epochs. To evaluate stability, it was trained ten times, yielding an average accuracy of 0.9616 ± 0.0024 (95% CI: [0.9601, 0.9630]), Macro F1 of 0.9527 ± 0.0021, and Weighted F1 of 0.9598 ± 0.0019, demonstrating consistent performance. A PHP-based web interface enables real-time predictions and adaptable deployment across hardware and cloud platforms. This system offers a scalable and accessible diagnostic tool for enhancing parasite detection and livestock health monitoring.</p>

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An artificial intelligence-based diagnosis system for the identification of helminth parasitic infections in mithun and allied bovines

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月17日

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  • Jayanta Kumar Chamuah,
  • Bikash Sarma,
  • Angughali Aheto Sumi,
  • Mahak Singh,
  • Harshit Kumar,
  • J. Arul Valan,
  • S. Girish Patil

摘要

This study presents a novel deep learning approach addressing the critical shortage of veterinary expertise in India’s North Eastern Hill (NEH) region through automated identification of parasitic infections in livestock. We developed a Convolutional Neural Network (CNN) architecture capable of analyzing both standard and microscopic images to identify and classify 16 distinct parasitic species. The model comprises four convolutional layers (32, 64, 128, 256 filters) with ReLU activation and MaxPooling for efficient feature extraction, followed by Dense layers and a Softmax classifier. The model was trained on a comprehensive dataset of over 5,334 annotated images, achieving 96% accuracy after 30 training epochs. To evaluate stability, it was trained ten times, yielding an average accuracy of 0.9616 ± 0.0024 (95% CI: [0.9601, 0.9630]), Macro F1 of 0.9527 ± 0.0021, and Weighted F1 of 0.9598 ± 0.0019, demonstrating consistent performance. A PHP-based web interface enables real-time predictions and adaptable deployment across hardware and cloud platforms. This system offers a scalable and accessible diagnostic tool for enhancing parasite detection and livestock health monitoring.