A deep learning method for identifying oral squamous cell carcinoma based on visual features

A squamous cell carcinoma and visual feature technology, applied in the field of deep learning technology, can solve problems such as artificial intelligence models for identifying oral squamous cell carcinoma, and achieve weight reduction and accurate classification

Active Publication Date: 2022-04-01
WUHAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

So far, no artificial intelligence model for identifying OSCC from visual features has been reported

Method used

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  • A deep learning method for identifying oral squamous cell carcinoma based on visual features
  • A deep learning method for identifying oral squamous cell carcinoma based on visual features
  • A deep learning method for identifying oral squamous cell carcinoma based on visual features

Examples

Experimental program
Comparison scheme
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Embodiment Construction

[0046] The present invention will be further described below in conjunction with the accompanying drawings.

[0047] Step 1. Obtain clear photos of oral squamous cell carcinoma lesions and normal oral photos, including: a development set for model training and parameter adjustment;

[0048] The test set is used for result evaluation.

[0049] The oral photos of patients with oral squamous cell carcinoma and healthy adults were taken with a digital SLR camera. Regardless of the camera brand, the shooting parameters were set as follows: manual mode, depth of field set to f value less than 1 / 18, and exposure time shorter than 1 / 80 Seconds, (converted into a 35mm format camera) macro lens between 100mm-105mm, the shooting camera is equipped with a ring macro flash (the brand is not limited), the flash is set to TTL mode, the camera’s white balance is set to the flash white balance, when taking photos Select the fixed central focus point to focus. Select the flash mode when the m...

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Abstract

The invention discloses a deep learning method for identifying oral squamous cell carcinoma based on visual features, including: 1) establishing a development set and a test set; 2) constructing data training samples; 3) training a target detection network model; 4) passing the The target detection model performs batch initial positioning on the oral photos of the development set, and constructs classification network data samples using the positioned image areas; 5) expands the classification network data samples; 6) DenseNet is used as the backbone network of the classification network, and DenseNet is improved to make the model Focus more on difficult misclassified samples; 7) train the classification network on the expanded samples to obtain an oral squamous cell carcinoma classification network; 8) test the classification network; 9) apply the classification network to classify oral photos. The method has high sensitivity and good specificity for detecting oral squamous cell carcinoma from oral cavity photographs, and is suitable for detection and screening of various oral squamous cell carcinomas.

Description

technical field [0001] The invention belongs to the field of medical image processing, and relates to the application of deep learning technology to the detection of specific visual feature regions in a certain type of photos. The deep learning neural network obtained from this training has good accuracy, precision and recall for the detection of oral squamous cell carcinoma in photos, and is suitable for the screening of patients with oral squamous cell carcinoma. [0002] technical background [0003] Oral cancer ranks among the top ten malignant tumors in the world. In 2018, there were 354,864 new cases of oral cancer predicted worldwide, and 177,384 deaths due to oral cancer. In terms of pathological classification, more than 90% of oral cancer patients are squamous cell carcinoma, and middle-aged and elderly men who are heavy smokers and alcoholics are high risk groups. Countries and regions where betel nut chewing is prevalent, such as India, Pakistan, Bangladesh, and...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06V10/25G06V10/774G06V10/82G06K9/62G06N3/04G06N3/08A61B5/00
CPCG06T7/0012G06N3/084A61B5/0088A61B5/4542A61B5/7267G06T2207/30096G06V10/25G06N3/045G06F18/214
Inventor 熊学鹏赵怡芳傅秋云李凯雄
Owner WUHAN UNIV
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