Several Applications of Convolutional Neural Networks in Medical Imaging

With the development of artificial intelligence, convolutional networks have powerful multi-dimensional data processing capabilities and can extract and process features in various images, which has great potential in the field of medical image processing. The development of convolutional neural networks has greatly promoted the development of computer aided diagnosis technology. This paper reviews the principle of four kinds of convolutional neural networks, including AlexNet, GoogleNet, U-Net, R-CNN, and their specific application research, such as diagnosis and analysis of brain tumors, classification of skin lesions, and detection of breast cancer. Compared with traditional convolutional networks, these new models have their own advantages and disadvantages. This paper also summarizes the advantages and disadvantages of these four neural networks. In the end, this paper also puts forward some current challenges in medical image research based on convolutional neural networks and the future prospects of medical image analysis technology combined with convolutional neural networks.