Esophageal cancer detection method and system based on barium meal radiography image
The invention provides an esophageal cancer detection method and system based on a barium meal contrast image. The method comprises the following steps: acquiring the barium meal contrast image of an esophagus to be detected; performing focus area detection on the barium meal contrast image accordin...
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creator | SHE YIFEI PAN ANSI XU SHENGZHOU LU HAORAN WU FUBIN |
description | The invention provides an esophageal cancer detection method and system based on a barium meal contrast image. The method comprises the following steps: acquiring the barium meal contrast image of an esophagus to be detected; performing focus area detection on the barium meal contrast image according to the trained target detection model; wherein the target detection model adopts an improved Faster R-CNN, and a basic network of the target detection model is composed of a convolutional neural network carrying an attention mechanism; obtaining a feature map of the image according to the basic network, and enhancing the feature saliency of the feature map through an attention mechanism; and generating a region of interest and a feature vector of the region of interest according to the feature map, and obtaining detection information of the lesion region according to the feature vector of the region of interest. The attention mechanism is embedded in the basic network, the capability of obtaining the features of |
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The method comprises the following steps: acquiring the barium meal contrast image of an esophagus to be detected; performing focus area detection on the barium meal contrast image according to the trained target detection model; wherein the target detection model adopts an improved Faster R-CNN, and a basic network of the target detection model is composed of a convolutional neural network carrying an attention mechanism; obtaining a feature map of the image according to the basic network, and enhancing the feature saliency of the feature map through an attention mechanism; and generating a region of interest and a feature vector of the region of interest according to the feature map, and obtaining detection information of the lesion region according to the feature vector of the region of interest. 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The method comprises the following steps: acquiring the barium meal contrast image of an esophagus to be detected; performing focus area detection on the barium meal contrast image according to the trained target detection model; wherein the target detection model adopts an improved Faster R-CNN, and a basic network of the target detection model is composed of a convolutional neural network carrying an attention mechanism; obtaining a feature map of the image according to the basic network, and enhancing the feature saliency of the feature map through an attention mechanism; and generating a region of interest and a feature vector of the region of interest according to the feature map, and obtaining detection information of the lesion region according to the feature vector of the region of interest. The attention mechanism is embedded in the basic network, the capability of obtaining the features of</abstract><oa>free_for_read</oa></addata></record> |
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language | chi ; eng |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
title | Esophageal cancer detection method and system based on barium meal radiography image |
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