Gastric cancer focus detection method and device based on convolutional neural network

Abstract

The invention relates to the technical field of image processing, and concretely relates to a gastric cancer focus detection method and device based on a convolutional neural network. The gastric cancer lesion detection method based on the convolutional neural network comprises the following steps: S1, preprocessing a general image of a gastric cancer sample to be detected; S2, performing focus target extraction and confidence analysis based on a target detection algorithm model, and outputting a focus detection result; or S3, finely segmenting and outlining the focus target based on the semantic segmentation algorithm model, and outputting a focus detection result. According to the method, the general image of the gastric cancer sample is utilized for the first time, the cancer lesion and intragastric or perigastric metastatic cancer lesion in the gastric resection specimen can be automatically positioned, meanwhile, the confidence coefficient of an analysis result is given, an examination doctor is assisted in accurately cutting a lesion part of the specimen, the cancer lesion detection efficiency is improved, and the missed diagnosis rate is reduced.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method and device for detecting gastric cancer lesions based on a convolutional neural network. Background technique [0002] Artificial intelligence (AI) is a branch of computer science dedicated to designing and executing computer algorithms that approximate human intelligence, so that computer algorithms can achieve similar working results to human intelligence when performing tasks. Machine Learning (ML) is a branch of the field of artificial intelligence, which refers to all non-explicit programming that enables machines to learn from data sets, predict positive events, and make decisions. Machine learning is divided into supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. Deep learning (deep learning, DL) belongs to the category of machine learning, and the most widely used deep learning model is convolutional...

AI Technical Summary

Problems solved by technology

However, the precise location of lesions in surgical resection specimens of early gastric cancer is one of the difficult problems in clinical pathological specimens at present. Sometimes it can only be identified by the experience of doctors, or rely on the surgeon to use sutures as marking prompts at suspicious lesions. Multiple lesions in the stomach or main lesions, multiple metastases in the stomach, and the identification of metastatic lymph nodes or cancer nodules around the stomach are recognized difficulties worldwide.

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