Application of artificial intelligence technology in medical imaging

Abstract

Based on massive medical image data, through reasonable system design and parameter optimization, the AI-assisted diagnosis system can help doctors make more clinical decisions, reduce their daily work pressure, reduce unnecessary invasive tests, and thus improve the quality of life of patients. This paper mainly studies the application of artificial intelligence technology in the field of medical imaging. In this paper, deeplesion public data set is used as the training set to study and optimize the convolutional neural network structure and parameters of deep learning algorithm based on the basic convolutional neural network model, aiming at the pathological features of lung cancer in the data set, such as lobulation sign and burr sign. At the same time, combining with the characteristics of neural network recognition and data set, the data set is preprocessed to highlight the features of the image and improve the accuracy of deep learning algorithm. Moreover, an auxiliary recognition system is developed around the optimized algorithm, so that the deep learning algorithm can intuitively show the automatic recognition of lung cancer through the recognition system after a large amount of data training.