Abstract: Chicken diseases are an important problem in the livestock industry, affecting the health and production performance of chicken flocks worldwide. These diseases can seriously damage the health of chickens, reduce egg production, or increase mortality, causing great economic losses to farmers. Therefore, detecting and preventing diseases in chickens is a top concern in the livestock industry, to ensure the health and sustainable production of chicken flocks. In recent years, advances in machine learning techniques have shown promise in solving challenges related to image diagnosis and classification. Leveraging the power of machine learning models, we propose the ViT16 model for disease classification in chickens, demonstrating its potential in assisting healthcare professionals to diagnose chicken flocks more effectively. In this study, ViT16 demonstrated its potential and strengths when compared with 5 models in the CNN architecture and ViT32 in the ViT architecture in the task of classifying chicken disease images with an accuracy of 99.25% - 99.75% - 100% - 98.25% in four experimental scenarios with our enhanced dataset and fine-tuning. These results were generated from transfer learning and model tuning on an augmented dataset consisting of 8067 images classified into four classes: Coccidiosis, New Castle Disease, Salmonella, and Healthy. Furthermore, the Integrated Gradients explanation has an important role in increasing the transparency and understanding of the image classification model, thereby improving and optimizing model performance. The performance evaluation of each model is done through in-depth analysis, including metrics such as precision, recall, F1 score, accuracy, and confusion matrix.

Huong Hoang Luong and Triet Minh Nguyen, “Improving Chicken Disease Classification Based on Vision Transformer and Combine with Integrated Gradients Explanation” International Journal of Advanced Computer Science and Applications(IJACSA), 15(4), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01504124

@article{Luong2024,
title = {Improving Chicken Disease Classification Based on Vision Transformer and Combine with Integrated Gradients Explanation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01504124},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01504124},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {Huong Hoang Luong and Triet Minh Nguyen}
}