Research On Automatic Detection Technology Of Pneumonia From Medical Images Based On Deep Learning

Deep learning has been widely used in natural language processing and image recognition.In recent years,a large number of researches involved automatic recognition and auxiliary diagnosis of medical images.Medical image recognition has become a hot spot of deep learning from the computer field to the medical field.Using deep learning to identify and detect medical images can not only alleviate the shortage of medical resources but also avoid the phenomenon of misdiagnosis and missed diagnosis caused by human factors.The use of computer-aided medical image diagnosis can greatly improve the efficiency of diagnosis.The epidemic is sweeping through the world,many researchers try to capture the image features of COVID-19 from the medical images of lung CT and X rays.That will help doctors diagnose novel coronavirus pneumonia to alleviate the problem of medical resource shortage.At present,novel coronavirus pneumonia CT recognition tasks using computers and deep learning technology have several difficulties,which lead to the fact that the existing models can not really be competent for this task.Firstly,the features of medical images are different from ordinary images,which have the characteristics of relatively simple semantics,but the focus are not obvious,so it is difficult to capture the features.In lung CT images,besides lung parenchyma,there are also many kinds of tissues,such as muscle,bone and so on.The focus of computer is shifted to these tissues rather than the lung parenchyma,which makes it difficult to improve the accuracy of recognition.Secondly,different models hold different features differently,and the accuracy of classification is different and unstable.Thirdly,it is time-consuming for doctors to label images manually.There are not a large number of labeled datasets,but only a small number of samples for machine learning.In view of the above problems,the contributions are as follows:(1)According to the characteristics of CT images,the paper proposes a lung segmentation algorithm based on Canny and non maximum suppression technology,which built the data set for deep learning training.(2)The recognition results of the existing classical convolution neural network model on the dataset is compared.Based on the results,COVID Net is proposed to fit the diagnosis of COVID 19,which greatly improves the efficiency of convolution neural network.And the COVID Net is optimized by Siamese Network,the recognition accuracy of convolution neural network model on the COVID 19 dataset is improved.(3)The COVID Net model is embedded on the Yolo V4 object detection network structure.The original feature extraction network Darknet 53 is optimized,and the automatic detection of the features of GGO area is achieved.It achieves SOTA in accuracy on the same task.In this paper,convolution neural network is improved for COVID 19 dataset,the accuracy and efficiency of the optimized model are improved on detection of COVID19.In addition,the object detection model proposed in this paper realizes the highprecision detection of the GGO area,which has important practical significance and clinical practice value.