In the development of modern social and economic construction, the level of scientific research and technology in various countries is getting higher and higher. The medical field began to use intelligent algorithms for detection and analysis, and achieved excellent results in practice. It is known that lung cancer is one of the most common diseases affecting human life and health, among which it ranks the first among male malignant tumor diseases and the second among female malignant tumor diseases. On the basis of understanding the research status of intelligent algorithms in the new era, this paper mainly studies the principle of tuberculosis detection methods with deep learning as the core according to the tuberculosis detection methods proposed by researchers in recent years. In the end, deep learning could help medical researchers detect more TB, improving the accuracy and sensitivity of actual tests.
Object detection is a hot topic in the field of machine vision. In the innovation and development of modern technology, the integration of machine learning, pattern recognition, image processing and other multidisciplinary knowledge can effectively solve the image problem. In computer vision and digital image processing technology in the development of innovation, target detection theory research and practical application has achieved excellent results, although there are many problems need to solve, but a lot of new technology and new method has yet to be developed, so countries scholars gradually strengthen the scientific research strength, strong adaptability are put forword technique, high precision and good robustness. In this paper, based on the understanding of the research status of object detection, according to the basic theory of image processing, the application of computer vision in object detection is analyzed. The final experimental results show that shadow detection, image enhancement and filtering methods can effectively improve the application effect.
In the medical field, the continuous monitoring of the probability of lung cancer incidence can be known that the actual growth rate is becoming faster and faster, and the complications become more and more serious. According to the research and analysis of experts in various fields, it is found that haze is one of the main factors leading to the occurrence of lung cancer in human body, especially in the urban environment. How to use the theory of artificial intelligence technology to design the automatic detection system of lung nodules is the core issue discussed in the field of medical science and technology at present. On the basis of understanding the status quo of convolutional neural network and lung cancer diagnosis and treatment, this study uses CAD system to complete the detection work from four steps, and combines practical cases to judge the detection performance of this technology, in order to lay a foundation for the development of technical innovation in the medical field.
To reduce the large data experiment platform construction cost and reduce the learning difficulty big data, this article is based on virtualization technology through the Docker software installed on the Linux system, using the Open VpN routing forwarding, using Java web technology, realizing the big data within the local area network (LAN) cluster environment fleetly, and constructed of lightweight data experiment platform. Through this platform, we can create a big data cluster with one key, provide a variety of experimental environments matching the courses, focus on the technology itself, and greatly improve learning efficiency. Experimental analysis shows that the proposed construction method has a host occupancy rate of around 10% and a memory occupancy rate of around 10%, and the system runs stably.
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