Mask RCNN-based gastric cancer early recognition method, system and device
A technology for early identification and gastric cancer, which is applied in the field of image processing, can solve the problems of poor efficiency of image analysis tools, inability to effectively distinguish cancerous and inflammatory features, and low accuracy, and achieve the effect of reducing the pressure on clinicians, fast speed, and high diagnosis
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Embodiment 1
[0062] This embodiment provides a method for early identification of gastric cancer based on Mask RCNN, such as figure 1 shown, including the following steps:
[0063] S1: Construct a gastric cancer recognition network for outputting cancerous or inflammatory feature regions. The input of the gastric cancer recognition network is gastroscopy images, and the output of the gastric cancer recognition network model includes inspection conclusions and image segmentation results of cancerous or inflammatory features.
[0064] The construction method of the gastric cancer recognition network includes the following process:
[0065] S11: Obtain the classic Mask RCNN network model, the backbone network of Mask RCNN is the ResNet50 network.
[0066] Such as figure 2 As shown, the Mask RCNN network model includes a feature extraction network, a region proposal network and a head network. The feature extraction network adopts the ResNet50 network, which is used to extract the features...
Embodiment 2
[0106] This embodiment provides an early identification system for gastric cancer based on Mask RCNN. The identification system uses the early identification method for gastric cancer based on Mask RCNN as in Example 1 to identify gastroscopy images, and gives the indications that there are "cancerous" and "inflammation". " or "normal" inspection conclusion, and give the image segmentation results of the identified cancerous or inflammatory features. Such as Figure 8 As shown, the gastric cancer early detection system includes:
[0107] The image acquisition unit is used to acquire video of gastroscopy examination, and divide the video data into image data for output; the image data is used as an input of a gastric cancer recognition network.
[0108] The gastric cancer recognition network includes a canceration recognition subunit, an inflammation recognition subunit, and a discrimination unit. The output image data of the image acquisition unit is simultaneously input int...
Embodiment 3
[0115] This embodiment provides a device for early detection of gastric cancer based on Mask RCNN, which includes a memory, a processor, and a computer program stored on the memory and operable on the processor. Steps of the system method for early detection of gastric cancer based on Mask RCNN.
[0116] The computer device can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a cabinet server (including an independent server, or a combination of multiple servers) that can execute programs. server cluster), etc. The computer device in this embodiment at least includes but is not limited to: a memory and a processor that can be communicatively connected to each other through a system bus.
[0117]In this embodiment, the memory (that is, the readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), random access memory (R...
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