Analysis and Application of Antibacterial Drug Resistance Based on Deep Learning

The continuous improvement of deep learning technology has led to its deeper application in related fields, especially in the detection of antimicrobial resistance in the medical field. In drug resistance detection, the CNN-ATT-TChan model based on the fusion of CNN algorithm and attention mechanism can classify and organize a large amount of antimicrobial resistance data, achieving standardized processing. Based on mature chemical analysis and testing methods, drug resistance test data was obtained, and the training duration and classification accuracy F of the model were discussed in combination with the test data. At the same time, based on relevant research literature, the changes in ROC curves and AUC values between different models were compared. The results showed that the CNN algorithm using fusion attention mechanism can improve the training time of the model and also improve the classification accuracy of the model. Therefore, the application of CNN-ATT-TChan model combined with attention mechanism in the detection of antimicrobial resistance provides more support for the development of antimicrobial resistance testing.

@article{Zhang2023,
  title     = {Analysis and Application of Antibacterial Drug Resistance Based on Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Wei Zhang and Yanhua Zhang and Qiang Zhang and Caixia Xie and Yan Chen and Jingwei Lei},
  doi       = {10.14569/IJACSA.2023.0141087},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141087}
}