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In recent years, artificial intelligence has been widely used in various fields, helping to solve many problems and getting good results. Machine learning is a very important part in the field of artificial intelligence. It has been successfully applied in many aspects, such as face recognition and voice recognition.
The principle of machine learning is to build an information model that can react or judge correctly through a large amount of data learning. Among them, medical image diagnosis is also an important application field of machine learning. If there is a good medical imaging artificial intelligence diagnosis system, it can effectively reduce misjudgment and provide more correct and timely treatment for patients. For example, take the medical image data of Computer Tomography (CT), a patient's primary CT will produce dozens to hundreds of medical image images. In addition to the judgment of professional medical personnel, a good model to help determine the content of medical image will speed up the processing speed and serve as the reference information for doctors to judge whether there is cancer or not.
In this paper, the open source data of medical imaging is used as the data source for machine learning and model building, mainly chest CT data in the format of DICOM (Digital Imaging and Communications in Medicine). Using convolution of deep learning Neural Network (Convolutional Neural Network CNN) on the basis of machine learning. Machine learning is not ideal to determine the outcome of cancer, because the medical imaging data and learning process must be more finely adjusted, and other physiological and medical structure information should be added. The huge amount of raw materials is also a problem that must be solved. These are all necessary tasks to improve diagnostic accuracy in the future.
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