Deep learning-based urine visible component detection method and device

A deep learning and component detection technology, applied in the field of medical image processing, can solve the problems of large target detection algorithm network model, slow reasoning, inability to achieve real-time detection, etc., to improve the detection effect, enhance the ability to distinguish, and reduce attention.

Pending Publication Date: 2022-02-11
ZHEJIANG UNIV OF TECH
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Problems solved by technology

The general-purpose target detection algorithm has a large network model, and it is easy to overfit if the general-purpose target detection algorithm is directly trained on the urine formed component data set
Moreover, the larger the network model, the more calculations are required, which will slow down the reasoning and fail to meet the requirements of real-time detection in practice.

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  • Deep learning-based urine visible component detection method and device
  • Deep learning-based urine visible component detection method and device
  • Deep learning-based urine visible component detection method and device

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Embodiment Construction

[0029] In order to make the purpose, technical solution and advantages of the present application clearer, the present application 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 application, not to limit the present application.

[0030] In one embodiment, such as figure 1 As shown, a deep learning-based urine formed component detection method is provided, including:

[0031] Step S1, input the urine sediment picture, extract features through the multi-scale feature extraction module, and output the feature maps of the last three layers of different sizes of the multi-scale feature extraction module, which are arranged in ascending order of size as the third layer feature map and the second layer feature map graph and the first layer feature map.

[0032] This application is a single-stage detection algorithm bas...

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Abstract

The invention discloses a deep learning-based urine visible component detection method and device. A single-stage target detection algorithm with higher detection speed is used, and a network model with fewer parameters is used; a local context attention module and a global background suppression module are provided for the global sparse and local dense characteristics of the urinary sediment image to improve the detection effect, so that the model pays attention to a region containing a target; the distinguishing capability of targets in a dense area is enhanced, and attention to the background is reduced. According to the technical scheme, for the urinary sediment picture, urine visible components in the picture can be effectively detected, and the situation of local dense distribution detection is greatly improved.

Description

technical field [0001] The application belongs to the technical field of medical image processing, and in particular relates to a method and device for detecting urine formed components based on deep learning. Background technique [0002] Object detection is one of the important research directions in the field of computer vision, and it is widely used in the fields of natural images, remote sensing images, and medical images. In recent years, many scholars have carried out a lot of work on object detection and made some progress. General-purpose detection algorithms are designed for complex natural image detection. In order to cope with the diverse characteristics and complex background of the target, the network models of these detection algorithms often have a lot of parameters. Urine sediment images are different from natural images. Urine sediment images are obtained by taking urine samples under a microscope, and the background of urine sample pictures contains almos...

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Application Information

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IPC IPC(8): G06T7/00G06V10/44G06V10/764G06V10/80G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06N3/08G06T2207/20076G06T2207/20081G06T2207/20084G06T2207/30004G06N3/047G06N3/045G06F18/2415G06F18/241G06F18/253
Inventor 产思贤郑竞成吴炳辉白琮陶健王红强
Owner ZHEJIANG UNIV OF TECH
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