Esophageal cancer lesion area search and identification modeling method based on evolutionary neural network structure

Abstract

The invention discloses an esophageal cancer lesion area search and identification modeling method based on evolutionary neural network structure, and relates to the technical field of image mode recognition and medical images, and the method comprises the following steps: S1, collecting and labeling an esophageal image data set for training a neural network model; S2, constructing a neural network structure search space for esophageal cancer lesion area identification; S3, training a super-network model for esophageal cancer lesion area identification; S4, searching an optimal neural networkstructure on the constructed super-network model by using an evolutionary algorithm; and S5, finely adjusting the searched neural network structure, and predicting a lesion area on the newly input esophageal image. The dependence of neural network structure design on expert experience in an esophageal cancer intelligent recognition task is eliminated, so that the deep neural network method is easier to use in the aspect of esophageal cancer lesion area recognition.

Description

technical field [0001] The invention relates to the technical field of image pattern recognition and medical images, in particular to a method for identifying and modeling esophageal cancer lesion regions based on evolutionary neural network structure search. Background technique [0002] Esophageal cancer (Esophageal Cancer, EC) is a malignant disease with a high mortality rate [A. K. Rustgiand H. B. El-Serag, “Esophageal cancer,” New England Journal of Medicine, vol. 371, no. 26, pp. 2499–2509 , 2014.], studies have shown that its 5-year survival rate is less than 20% [ X.-X.Chen, Q. Zhong, Y. Liu, S.-M. Yan, Z.-H. Chen, S.-Z . Jin, T.-L. Xia, R.-Y.Li, A.-J. Zhou, Z. Su et al., “Genomic comparison of esophageal squamous cellcarcinoma and its precursor lesions by multi-region whole-exome sequencing ,” Nature Communications, vol. 8, no. 1, pp. 1–12, 2017.]. Gastroscopy has been widely used in the diagnosis of early esophageal cancer, which can provide guidance for early in...

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Problems solved by technology

[0006] In general: the convolutional neural network method has been widely used in the identification of esophageal cancer lesion regions, but the construction of the network model has a strong dependence on the experience of doctors and neural network experts

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