Application of Network Image Enhancement Integrating Artificial Intelligence in Chronic Disease Management

Chronic diseases are a global health problem that and more scholars have conducted research on image enhancement behavior recognition technology, mainly including image feature extraction, image modeling, feature selection, etc. The feature extraction technology of images is one of the important techniques for image denoising. The grayscale and grayscale values of each pixel in the image are represented through the gray domain. In current clinical treatments, pharmacists face many complex problems when performing image fusion. For example, when a pharmacist enhances a pharmacological imaging, the image becomes disorganized, making it difficult to accurately obtain the clarity and contrast of the image, and it is difficult for pharmacists to use imaging to determine whether a specific drug is effective for a patient's disease. Due to the above problems, image recognition algorithms came into being. In this paper, image based methods have been proposed. This article first provides a detailed introduction to the basic framework of artificial intelligence, then applies artificial intelligence to network image enhancement behavior recognition in clinical treatment, tests the overall efficiency and accuracy of data, and finally uses a survey questionnaire for medical staff to demonstrate that artificial intelligence is very helpful in improving the application research of image enhancement behavior recognition in clinical treatment of chronic disease management. Therefore, Making good use of artificial intelligence for network image enhancement behavior recognition is a highly worthy research topic in clinical treatment of chronic disease management.