Deep learning face diagnosis system

Abstract

The invention provides a face diagnosis method and system based on deep learning. The method includes the steps of using a large number of human face images with disease labels for training a deep learning network, carrying iteration for multiple times, after parameters updating, the deep learning network being capable of automatically and effectively extracting the facial features including eyes,ears, mouths and eyebrows, and finding the internal relation between the facial features and various diseases of the human body so as to carry out effective disease detection and screen on the face images of new subjects. The method is a non-invasive computer-aided automatic diagnosis method. According to the system, the parameters can be updated by continuously collecting the facial images of patients and training, so that the prediction judgment result is more accurate. By means of the method and the system, the problem of difficulty in diseases inspection in poverty-trapped regions can beeffectively solved, so that people can conveniently and rapidly carry out non-invasive disease automatic detection and screening through a mobile phone, computer and other terminals, and the diseasescan be treated in time. The people's living quality is improved.

Description

technical field [0001] The invention belongs to the fields of computer vision, machine learning and medicine, and in particular relates to a method and system for diagnosing diseases through faces based on deep learning. Background technique [0002] More than 2,000 years ago, the ancient Chinese book "Huangdi Neijing" recorded that "there are twelve meridians, three hundred and sixty-five channels, all of which originate from the blood on the face and go through the aperture." This shows that the pathological changes of the internal organs of a person will be manifested in the relevant areas of the face. In China, experienced doctors can grasp the patient's general and local lesions by observing the facial features. This diagnosis method is called "face-to-face diagnosis". The disadvantage of face-to-face diagnosis is that this diagnosis method requires a lot of experience of doctors to have a relatively high accuracy rate. [0003] With the development of science and tec...

AI Technical Summary

Problems solved by technology

In 2014, Google's and Oxford University's Visual Geometry Group each used their deep convolutional neural networks GoogleNet and VGGNet to achieve excellent results in ILSVRC. These two deep convolutional neural networks are more complex in structure than AlexNet.

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