Predictive COVID-19 Risk and Virus Mutation isolation with CNN based Machine learning Technique

Abstract

In the research of applying deep learning to CT intelligent recognition of new coronary pneumonia, many researchers have built deep neural network training models on understanding the content of medical image data and assisting in the diagnosis of new coronary pneumonia. The AMDRC-Net architecture is proposed, in which the residual structure solves the problem of network degradation through identity mapping. At the same time, for the new situation that the residual system hinders the exploration of new features, inspired by the latest research such as attention mechanism, the research length Attention Guidance Mechanism. First, focus on the security of the deep learning model discuss the adversarial attack method based on gradient ascent; to solve the problem of its singularity, the long and short attention mechanism is used to increase the effective adversarial disturbance while reducing the redundant disruption. Next, the proposed adversarial attack algorithm AAS transforms the adversarial attack problem into an adaptive constraint problem; that is, the micro-transformation idea is used in the iterative attack, and the relationship between the attention guidance mechanism the DNN adversarial attack is explored. In the last experiment, on the CT data set of new coronary pneumonia, AMDRC-Net is used for model training, and comparison experiments, visualization experiments, and adversarial attack experiments are designed.

Ähnliche Arbeiten