Facial Computer Aided Diagnos Search Of OSAHS

As for obstructive sleep apnea hypopnea syndrome in children,it refers to part or all of the upper airway obstruction that occurs frequently in children during sleep,which thus causes a series of physiological and pathological changes interfering with the normal ventilation breathing and sleep structure of children.Widely existing in children from 2 to 10 years old,this disease is not easy to arise the attention of people,while it will affect the growth and intellectual development of children if it is not treated until a long time.In the meantime,it has the features of catholicity,universality,the difficulty to be found,great harm,and so on.At the current stage,the clinical diagnosis method has met some problems as follows,such as high cost,poor accuracy,and so on,while children with OSAHS need to be diagnosed as soon as possible for further treatment.Thus,a computer-aided diagnosis method based on face image is proposed in this thesis,in which the main work covers the following three parts:(1)Methods for data collection and preprocessing.The data collection app is developed,and the cooperation with the Sleep and Respiratory Center of Beijing Children’s Hospital is conducted to collect daily patient data over a long period of time.Then,the original data is collected and sorted out.By taking advantage of digital image processing technology,it preprocesses the original face data,which covers artificial screening,face alignment correction and centralization,grayscale,image enhancement and illumination compensation.Finally,it obtains 2121 effective cases and 8440 images,which are used in the research next step.(2)Classification research of feature extraction and selection methods.The global and local features of the processed face data is extracted,which covers the features of contour edge,texture wavelet,Gabor and so on.Local features include various empirical features,which can automatically extract various physiological features of the face.After a new round of dimensionality reduction and combination,it uses various classifiers to carry out classification,which includes decision tree,support vector machine,clustering method,promotion and integrated learning method.Then,the experiments are conducted by selecting different principal components in line with various features.According to the result,it show the most accurate rate reaches 91.3%.The research is carried out based on the feature extraction method of deep convolution neural network technology.Through the design of the network,it finds the unique features of the face of OSAHS patients,which conducts explanatory research on key areas.According to the analysis,it finds that upper lip as a unique feature of OSAHS patients varies from ordinary normal cases,which can be referred to for further clinical diagnosis.