Lung focus identification method and device and realization device

Abstract

The invention provides a lung focus identification method and device and a realization device. The lung focus identification method comprises steps of obtaining a CT image of a lung, inputting the CTimage into a first identification model and a second identification model which are established in advance, outputting a corresponding first identification result and second identification result, wherein the first identification model is obtained by training a neural network for identifying a focus area having a size greater than a first threshold value and the second identification model is obtained by training the neural network for identifying a focus area having a size smaller than a second threshold value, and determining a final lung focus identification result according to the first identification result and the second identification result. The lung focus identification method and device and the realization device can identify the focus areas of different sizes through different identification models of different neural networks, and can comprehensive identify the areas of big focus and small focus in the CT image while guaranteeing an operation rate.

Description

technical field [0001] The invention relates to the technical field of medical imaging, in particular to a lung lesion identification method, device and implementation device. Background technique [0002] Lung cancer is the most common cancer in the world, and its incidence rate is increasing year by year. The survival rate of lung cancer is highly related to the stage of the disease when it is first diagnosed. Therefore, imaging diagnosis of lung cancer is an important means to detect early lung cancer, but it is limited by equipment, experience, objective recognition ability of human eyes, and the impact of overload. Doctors will inevitably miss and misdiagnose; apply big data-driven artificial intelligence to early lung cancer imaging diagnosis, and use machines to assist doctors to improve the efficiency of diagnosis and treatment, which can alleviate the overloaded work status of doctors, reduce missed and misdiagnosed diagnoses, and improve the quality of doctors' dia...

AI Technical Summary

Problems solved by technology

[0003] Lung lesions are mostly identified through CT images; the existing CT image recognition methods can identify large sized and distinctive lesion areas through image detection, machine learning and other means, but it is difficult to identify small sized or indistinct lesions Larger, it is still difficult to effectively solve the problem of missed diagnosis

[0004] Aiming at the problem that the above-mentioned existing methods for identifying lung lesions are incomplete and prone to omissions, no effective solution has been proposed

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