Abstract
Poultry diseases are one of the significant issues that need to be addressed in poultry farming today. In order to contribute to the prevention and control of poultry diseases and promote the healthy development of poultry farming, this paper focuses on a collected dataset of chicken manure images and proposes an improved chicken disease image recognition model based on ResNet18. The model utilizes ResNet18 as the underlying framework and incorporates attention mechanisms into the ResNet18 network model. It also includes a fully connected layer (Fc1) and Dropout for enhanced performance. Transfer learning is employed to train the model by freezing some layers of the pre-trained model to reduce training time. The Adam optimization algorithm is used to update gradients, and a cosine annealing method is implemented to decay the learning rate. Experimental results demonstrate that the improved ResNet18 network achieves an accuracy of 97.81% in chicken disease image recognition, which is 1.27% higher than the accuracy of the original ResNet18 network. This improved model exhibits superior performance and provides valuable insights for the analysis of chicken disease images.
Supported by the National Natural Science Foundation of China under Grant U22A20102; the Key Science and Technology Research Program of Chongqing Municipal Education Commission, China (Grant No. KJZD-K202101305); Natural Science Foundation of Chongqing, China (Grant No. cstc2021jcyj-msxmX0495, cstc2021jcyj-msxmX0654); the Yingcai Program of Chongqing, China(Grant No. cstc2021ycjh-bgzxm0218).
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Zhang, N., Ma, X., Huang, Y., Bai, J. (2023). Image Recognition of Chicken Diseases Based on Improved Residual Networks. In: Li, Y., Huang, Z., Sharma, M., Chen, L., Zhou, R. (eds) Health Information Science. HIS 2023. Lecture Notes in Computer Science, vol 14305. Springer, Singapore. https://doi.org/10.1007/978-981-99-7108-4_22
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