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Real-time colonoscope polyp detection device based on deep learning

A detection device and colonoscopy technology, which is applied in the interdisciplinary field of medicine and computer science, can solve problems such as difficulty in model convergence and affecting the distribution of data sets, and achieve the effect of improving model accuracy, improving accuracy, and reducing the amount of calculation

Pending Publication Date: 2021-04-20
杭州优视泰信息技术有限公司
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Problems solved by technology

However, the improvement of this method is limited. Too much data enhancement will affect the distribution of the data set, which will lead to problems such as difficult convergence of the model.

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  • Real-time colonoscope polyp detection device based on deep learning
  • Real-time colonoscope polyp detection device based on deep learning
  • Real-time colonoscope polyp detection device based on deep learning

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Embodiment Construction

[0036] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and do not limit the protection scope of the present invention.

[0037] The embodiment provides a real-time colonoscopy polyp detection device based on a deep convolutional neural network, comprising a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, which It is characterized in that the computer memory stores a colonoscopy polyp detection model including a feature extraction unit, an attention prediction unit and a detection unit and a spatial attention feature map corresponding to the last frame of colonoscopy image, using the colonoscopy polyp detec...

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Abstract

The invention discloses a real-time colonoscope polyp detection device based on a deep convolutional neural network, and the real-time colonoscope polyp detection process comprises the steps: obtaining a to-be-detected current frame of colonoscope image, and inputting the to-be-detected colonoscope image into a colonoscope polyp detection model; after the image feature map of the enteroscope image to be detected is extracted by using the feature extraction unit, predicting and outputting the spatial attention feature map corresponding to the current frame of enteroscope image by using the attention prediction unit according to the image feature map and the spatial attention feature map corresponding to the previous frame of enteroscope image; and predicting and outputting a detection result by using a detection unit according to the image feature map and the spatial attention feature map corresponding to the previous enteroscope image. The real-time enteroscopy polyp detection device can improve the accuracy and efficiency of clinical enteroscopy.

Description

technical field [0001] The invention belongs to the interdisciplinary field of medicine and computer science, and in particular relates to a deep learning-based colonoscopy polyp detection device. Background technique [0002] Colorectal polyps are abnormal tumors raised on the surface of the colorectum, which have a certain risk of malignant transformation and can lead to colorectal cancer. At present, the most widely used and effective diagnostic method is to examine the intestinal tract with an endoscope. Modern endoscopes are generally equipped with a camera at the end, which can transmit the collected images to a computer for display. Doctors can find polyps and other lesions by examining the endoscope images to determine the patient's condition. In the current endoscopic examination process, the doctor mainly relies on the naked eye to observe the endoscopic image to detect polyps. The detection rate is affected by the doctor's experience and status, and there is a p...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08
Inventor 史勇强顾梦奇
Owner 杭州优视泰信息技术有限公司
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