Prediction method for early-stage lung cancer based on deep learning, and electronic equipment

Abstract

The invention discloses a prediction method for an early-stage lung cancer based on deep learning. The method comprises the following steps of acquiring lung CT (Computed Tomography) image data, wherein the lung CT image data consist of a plurality of lung slices; performing a pretreatment operation on the lung CT image data; designing a lung nodule detection model; designing a lung nodule classification model; and training the lung nodule detection model according to a sample number in each batch. According to the prediction method for the early-stage lung cancer based on deep learning, the original CT data are converted into model trainable data through a preprocessor, the trained model is deployed in a server, a lung nodule of a patient whose CT data are uploaded is detected by use of the model of the server, a nodule position and a nodule size are output, the benignness and malignancy of the nodule are detected and prediction is performed on the nodule state of next check.

Description

technical field [0001] The present invention relates to CT imaging technology, in particular to an early prediction method and electronic equipment for lung cancer based on deep learning. Background technique [0002] Lung cancer is one of the malignant tumors with the highest incidence rate among malignant tumors, and its five-year survival rate is only about 15%. Therefore, early detection and treatment can improve the cure rate. The early symptoms of lung cancer are very inconspicuous and can be easily ignored. In the advanced stage, cancer cells may metastasize, making treatment very difficult. A series of studies by the American Cancer Society have shown that detecting lung nodules is a very effective means of early detection of lung cancer. Due to the small size of lung nodules, early screening by low-dose CT can greatly improve the diagnosis rate of early lung cancer. [0003] CT tomography is a very high-resolution three-dimensional imaging with a huge amount of d...

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Problems solved by technology

[0005] In order to overcome the deficiencies of the prior art, one of the purposes of the present invention is to provide an early prediction method for lung cancer based on deep learning, which can solve the problem of low efficiency of lung cancer diagnosis in the prior art

[0006] The second object of the present invention is to provide an electronic device, which can solve the problem of low diagnostic efficiency of lung cancer in the prior art

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