Artificial intelligence diagnosis method for gastric cancer under narrow-band imaging amplification gastroscope

Abstract

The invention relates to the technical field of medical technology assistance, in particular to an artificial intelligence diagnosis method for gastric cancer under a narrow-band imaging amplificationgastroscope, which comprises the following steps: S1, constructing a mini-UNet neural network model; S2, constructing a UNet + + image segmentation neural network model, and obtaining an area ratio Rabnormal of an image feature difference region; S3, adopting a generative adversarial network (GAN) technology to obtain a microvascular morphological diagram and a microstructure morphological diagram of the feature abnormal region; S4, identifying the microvessel form dissimilarity degree and the microstructure form dissimilarity degree in the microvessel form diagram and the microstructure formdiagram by the neural network model ResNet50; and S5, carrying out identification and judgment by using the random forest model obtained by training to obtain final judgment of cancer or non-cancer,and identifying the canceration position range of the image, which is judged to be cancer, as the identified image feature difference region Pabnormal. The sensitivity and specificity of the kit to cancer and non-cancer recognition reach about 93.4% and 90.7% respectively, a clinician can be effectively assisted in discriminating and diagnosing cancer and non-cancer, and a canceration position range is given.

Description

technical field [0001] The invention relates to the field of medical technology auxiliary technology, in particular to an artificial intelligence diagnosis method for gastric cancer under a narrow-band imaging magnifying gastroscope. Background technique [0002] Gastric cancer is a malignant tumor of the gastric mucosal epithelium. Endoscopy is the most important means of gastric cancer screening, especially the Magnifying Endoscopy with Narrow Band Imaging (ME-NBI) technology can capture high-definition enlarged gastric mucosal images , Doctors are of great help to accurate diagnosis by observing the extent of the lesion area in the image, the microvessels and microstructure of the lesion area. [0003] In recent years, with the rapid development of computer artificial intelligence technology, the application of artificial intelligence, deep learning, convolutional neural network and other technologies in the medical field has gradually increased, and they are used for aux...

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However, the microvessels and microstructure diagrams displayed by the invention patent ar

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