IEDO-net: Optimized Resnet50 for the classification of COVID-19

The emergence of COVID-19 has broken the silence of humanity and people are gradually becoming concerned about pneumonia-related diseases; thus, improving the recognition rate of pneumonia-related diseases is an important task. Neural networks have a remarkable effectiveness in medical diagnoses, though the internal parameters need to be set in accordance to different data sets; therefore, an important challenge is how to further improve the efficiency of neural network models. In this paper, we proposed a learning exponential distribution optimizer based on chaotic evolution, and we optimized Resnet50 for COVID classification, in which the model is abbreviated as IEDO-net. The algorithm introduces a criterion for judging the distance of the signal-to-noise ratio, a chaotic evolution mechanism is designed according to this criterion to effectively improve the search efficiency of the algorithm, and a rotating flight mechanism is introduced to improve the search capability of the algorithm. In the computed tomography (CT) image data of COVID-19, the performed. The results show that the diagnostic performance of IEDO-net is competitive, which validates the feasibility and effectiveness of the proposed network.