Application of Artificial Intelligence-Based Digital Image Processing Technology in Medical Image Analysis
Artificial intelligence software works far more efficiently than the human brain, which not only can find patterns
and similarities of data, but also can help doctors and scientists extract important information. This paper proposes
a digital image processing technology based on artificial intelligence and its application in medical image analysis.
CT image uses the deep learning theory to denoise. In the image denoising problem, since the input layer and the
output layer of the neural network are both the same image size as the original image, if the pooling layer is added
to the network, a bottleneck is formed, which affects the recovery of the final image. In order to increase the
receptive field of the neural network without adding a pooling layer, this paper replaces the large-scale symmetric
convolution with asymmetric convolution. In this paper, a multi-feature residual learning module combines the
Inception module with residual learning, so that the network can extract richer image information and the network
is closer to the sparse connection. Experiments show that the model can achieve good visual effects in the
denoising experiment of real CT images. It can protect the edge information and texture information of Medical
images while denoising, and effectively suppress the artifacts in the image. With the development of artificial
intelligence and its gradual popularization and application in the medical field, the integration of artificial
intelligence and digital image processing technology will become an important direction of medical development
in the future.