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Early cancer auxiliary diagnosis system based on artificial intelligence

An auxiliary diagnosis and artificial intelligence technology, applied in the field of image analysis, can solve the problems such as the inability to guarantee the diagnosis accuracy of artificial intelligence algorithms and the difficulty in achieving the training effect, and achieve the effect of improving the recognition accuracy.

Active Publication Date: 2019-11-29
CHONGQING UNIV CANCER HOSPITAL
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AI Technical Summary

Problems solved by technology

Due to the differences between the recognition of artificial intelligence algorithms and human visual recognition, artificial intelligence algorithms may not necessarily recognize pictures that are easy for humans to recognize. If the format of the sample image is difficult for artificial intelligence algorithms to recognize, then no matter how many sample images are used for training, it is difficult to achieve the ideal training effect, and the accuracy of artificial intelligence algorithm diagnosis cannot be guaranteed

Method used

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  • Early cancer auxiliary diagnosis system based on artificial intelligence

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0036] Such as figure 1 As shown, an artificial intelligence-based early cancer auxiliary diagnosis system includes an image acquisition module, a model building module and a diagnosis module.

[0037] The image acquisition module is used to acquire marked sample images of digestive tract endoscopy, preprocess and randomly sort the sample images, generate a training image set, and perform normalization processing on all sample images in the training image set. In this implementation, the normalization process refers to normalizing the sample image into DICOM format, NIfTI format or original binary format.

[0038] In this embodiment, the sample image includes one or more cancer categories in early esophageal cancer, early gastric cancer, and early colon cancer, and each cancer category corresponds to a morphology subcategory and an infiltration depth subcategory; the sample image of the morphology subcategory There are no less than 5,000 sample images in the subcategory of im...

Embodiment 2

[0051] An artificial intelligence-based early cancer auxiliary diagnosis system differs from Embodiment 1 in that when the construction unit outputs the convolutional neural network model, the image acquisition module marks the adjusted preprocessing method as an effective preprocessing method. The image acquisition module is also used to preprocess the image to be diagnosed by an effective preprocessing method after acquiring the image to be diagnosed from the gastrointestinal endoscope, and send the preprocessed image to be diagnosed to the diagnosis module. The diagnosis module is used to receive the image to be diagnosed of the digestive tract endoscope, judge the image to be diagnosed based on the successfully trained convolutional neural network model, and output the judgment result. In this embodiment, the judgment result is normal or early cancer.

[0052] The image to be diagnosed is preprocessed through an effective preprocessing method, so that the image to be diagn...

Embodiment 3

[0054] An artificial intelligence-based early cancer auxiliary diagnosis system differs from Embodiment 2 in that it also includes a judgment module and an environment regulation module;

[0055] The evaluation module is used to obtain the judgment result of the doctor, compare the judgment result of the doctor in the same image to be diagnosed with the judgment result of the diagnosis module, and judge whether they are consistent. If they are inconsistent, the evaluation module outputs the request evaluation information; Carry out auxiliary diagnosis, reduce the probability of misdiagnosis through double verification, and improve the diagnosis efficiency of doctors at the same time.

[0056] The judging module is also used to receive judged information; in this embodiment, the judged information is that the doctor is correct or the convolutional neural network model is correct. When the judging information is inconsistent, the judging module outputs the request judging inform...

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Abstract

The invention relates to the technical field of image analysis, and particularly discloses an early-stage cancer auxiliary diagnosis system based on artificial intelligence, and the system comprises:an image obtaining module which is used for obtaining a marked sample image of a digestive tract endoscope, carrying out the preprocessing and random sorting of the sample image, and generating a training image set; a model construction module which is used for constructing a convolutional neural network model, carrying out iterative training on the convolutional neural network model based on thetraining image set, then carrying out testing, and outputting the successfully trained convolutional neural network model after the testing is completed; and a diagnosis module which is used for acquiring a to-be-diagnosed image of the digestive tract endoscope, judging the to-be-diagnosed image based on the successfully trained convolutional neural network model, and outputting a judgment result.By adopting the technical scheme of the invention, the training effectiveness can be improved.

Description

technical field [0001] The invention relates to the technical field of image analysis, in particular to an artificial intelligence-based early cancer auxiliary diagnosis system. Background technique [0002] Early detection and early treatment of digestive tract cancer have very important practical significance. However, the distribution of gas in the digestive tract is more, the lesions are smaller and more occult, and the morphology, surface microstructure, and surface microvessels of early cancer are very similar to inflammation and repair, which makes the pathological characteristics of digestive tract cancer complex and difficult. Identification, early symptoms are not easy to be found. [0003] At present, the diagnosis of digestive tract cancer mainly collects images in the human body through the optical lens and image sensor of the digestive endoscope, and transmits the collected images to the display terminal for viewing by medical staff. Gastrointestinal endoscop...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/10068G06T2207/20081G06T2207/20084G06T2207/30096
Inventor 陈伟庆柴毅
Owner CHONGQING UNIV CANCER HOSPITAL
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