Lung CT Image Classification Algorithm Based on Improved Inception Network

With the continuous development of digital technology, traditional lung computed tomography medical image processing has problems such as complex images, small sample data, and similar symptoms between diseases. How to efficiently process lung computed tomography image classification has become a technical challenge. Based on this, the Inception algorithm is fused with the improved U-Net fully convolutional network to construct a lung computed tomography image classification algorithm model based on the improved Inception network. Subsequently, the Inception algorithm is compared with other algorithms for performance analysis. The results show that the proposed algorithm has the highest accuracy of 92.7% and the lowest error rate of 0.013%, which is superior to the comparison algorithm. In terms of recall comparison, the algorithm is approximately 0.121 and 0.213 higher than ResNet and GoogLeNct algorithms, respectively. In comparison with other models, the proposed model has a classification accuracy of 98.1% for viral pneumonia, with faster convergence speed and fewer required parameters. From this result, the proposed Inception network based lung computed tomography image classification algorithm model can efficiently process data information, provide technical support for lung computed tomography image classification, and thereby improve the accuracy of lung disease diagnosis.

@article{Liu2024,
  title     = {Lung CT Image Classification Algorithm Based on Improved Inception Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Qianlan Liu},
  doi       = {10.14569/IJACSA.2024.0151023},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151023}
}