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A detection method for seven types of skin tumors applied to the web

A technology for skin tumors and detection methods, applied in applications, diagnostic records/measurements, instruments, etc., can solve problems such as insufficient medical resources, and achieve the effect of simplifying the process, accurate results, and convenient testing

Active Publication Date: 2021-02-09
HANGZHOU DIANZI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the serious shortage of medical resources at present, the present invention proposes a detection method for seven types of skin tumors applied to mobile phones

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  • A detection method for seven types of skin tumors applied to the web
  • A detection method for seven types of skin tumors applied to the web
  • A detection method for seven types of skin tumors applied to the web

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Embodiment Construction

[0025] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0026] The hardware environment used for implementation is: CPU Intel(R) Xeon(R) CPU E5-2630 v4@2.20GHz, GPU is NVIDIA K80, and the operating environment is python2.7 and TensorFlow.

[0027] The deep convolutional neural network Inception-ResNetV2 is adopted, the global average pooling layer is used to replace the fully connected layer, and the space is decomposed into asymmetric convolution, which greatly reduces the amount of parameters and speeds up the calculation.

[0028] The excellent performance of this network is mainly due to the extensive use of dimensionality reduction processing. The convolution of large-size filters (such as 5*5, 7*7) introduces a large amount of calculations. Using two 3*3 convolutions in series instead of a 5*5 convolution can reduce the amount of calculations, and the 3 The *3 convolution kernel is decomposed...

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Abstract

The invention discloses a method for detecting seven types of skin tumors applied to the Web. The invention combines the weight of the model with the Web end based on the model migration technology mediated by Tensorflow, so that the model can run normally and quickly in the browser environment familiar to users. , while visualizing the model results. The model uses the optimized Inception‑ResNetV2 classification network, and proposes a skin tumor classification method for skin tumor images. This method can detect seven types of skin tumors and achieve ideal results, which can effectively assist doctors in diagnosis. The web-side development method for the detection of seven types of skin tumors proposed by the invention simplifies the process of detecting skin tumors, and responds the obtained probability results to the client browser, and the browser renders and visualizes them, so that doctors and patients only need to simply operate the web The test results can be obtained in real time.

Description

technical field [0001] The invention relates to the field of artificial intelligence, and relates to a method for detecting seven types of skin tumors applied to a web terminal. Background technique [0002] Skin cancer is a malignant tumor that occurs on the surface of the skin. It is also one of the most common cancers. There are about 2 to 3 million new cases of cancer worldwide every year, resulting in more than 50,000 deaths. However, as long as the malignant skin tumor can be diagnosed at an early stage, regardless of surgery, radiotherapy or other treatment methods, it has a good curative effect on malignant skin tumors, and the cure rate can be over 90%. [0003] The deep learning algorithm has attracted people's attention since its success in 2006. Compared with non-deep learning algorithms in many fields, the overall effect is 20%-30% higher. In recent years, breakthroughs in algorithms have emerged one after another, making the deep learning model more and more p...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62A61B5/00
CPCA61B5/444A61B5/7267G06V2201/03G06F18/214
Inventor 端阳王飞孙玲玲吕岱霖李智星
Owner HANGZHOU DIANZI UNIV