A system and method for image diagnosis of lung cancer based on big data

Abstract

The invention belongs to the technical field of medical diagnosis, and discloses a lung cancer image diagnosis system and method based on big data. The image acquisition module uses the gray threshold method to segment the image, and uses the mathematical morphology method to repair the defects of the binary image. The code tracks and stores the contour information, extracts the image contour with a single pixel edge, and obtains the user's image data information; collects the user's exhaled gas through the breath collection module; detects the user's body temperature data through the body temperature detection module. The image diagnosis method for lung cancer based on big data of the present invention has strong operability, and the image diagnosis method is easy to carry out. It is widely used in practice and can be applied to image data processing in other fields. The image diagnosis method of the present invention has strong robustness; it can improve the accuracy of predicting early-stage lung cancer; at the same time, the cloud service module can greatly improve the analysis and calculation speed of diagnosis data and improve the diagnosis efficiency.

Description

technical field [0001] The invention belongs to the technical field of medical diagnosis, and in particular relates to a big data-based lung cancer image diagnosis system and method. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] Lung cancer is one of the malignant tumors with the fastest increasing morbidity and mortality and the greatest threat to the health and life of the population. In the past 50 years, many countries have reported that the morbidity and mortality of lung cancer have increased significantly. The incidence and mortality of lung cancer in men rank first among all malignant tumors, and the incidence and mortality of lung cancer occupy the second place in women. The etiology of lung cancer is still not completely clear. A large number of data show that long-term heavy smoking has a very close relationship with the occurrence of lung cancer. Existing studies have shown that long-...

AI Technical Summary

Problems solved by technology

[0006] The classic edge detection method is local operator method, such as gradient operator, Sobel operator, Roberts operator, Canny operator, etc. This method has the advantages of simple implementation and fast operation speed, but has the following disadvantages: the detected The edge cannot be guaranteed to be a single pixel wide, and there are often isolated or only continuous edges in small segments

[0008] The existing image data for lung cancer diagnosis is inaccurate; at the same time, the processing speed of lung cancer diagnosis data is slow and the diagnosis efficiency is low

[0009] The existing image diagnosis method of lung cancer based on big data has poor operability and complex image diagnosis method

In practice the application is limited

[0010] The traditional edge detection method has the characteristics of weak anti-interference and low precision, which cannot meet the actual needs of medical engineering image technology.

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