Dense neural network lung tumor image recognition method fusing multi-scale features
The invention discloses a dense neural network lung tumor image recognition method fusing multi-scale features, and the method comprises the steps: collecting and preprocessing CT modal medical images, extracting lesion ROI regions of different scales, and forming a multi-scale data set; wherein the...
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Main Authors | , , , , |
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Format | Patent |
Language | Chinese English |
Published |
09.02.2021
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Subjects | |
Online Access | Get full text |
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Summary: | The invention discloses a dense neural network lung tumor image recognition method fusing multi-scale features, and the method comprises the steps: collecting and preprocessing CT modal medical images, extracting lesion ROI regions of different scales, and forming a multi-scale data set; wherein the focus ROI regions of different scales are provided with clinically marked benign or malignant tumortags; training the multi-scale data set in a dense neural network, constructing a dense neural network model, extracting a full connection layer feature vector and carrying out feature serial fusion;and obtaining a lung tumor classification result in the NSCR classifier. A dense neural network model constructed by the method is superior to an AlexNet model, bottom-layer new features can be minedagain by effectively utilizing high-layer information, transmission of the features among networks is enhanced, and feature reuse is realized and enhanced; the invention is deep in network depth, strong in network generalizati |
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Bibliography: | Application Number: CN202011254411 |