Research On Segmentation Of Fundus Images Based On Deep Neural Network Algorithm

Retinal edema-related lesions of the fundus are one of the main causes of visual impairment and blindness in adults,and its incidence will increase significantly with age.If retinal edema lesions can be screened at an early stage and get timely treatment,vision can be restored.Therefore,early screening for retinal edema-related diseases of the fundus is extremely important.At present,for the diagnosis of retinal edema-related lesions of the fundus,doctors mainly use experience to judge the collected images of the patient’s fundus.With the public’s increasing requirements for precision medicine and medical imaging equipment acquiring more and more medical images,it is not enough to meet the medical needs of doctors to diagnose fundus diseases through personal experience.The use of digital image processing technology for early screening,diagnosis and condition analysis of retinal edema-related lesions of the fundus is very important.Therefore,this research proposes a method for semantic segmentation of fundus images based on deep neural network algorithms,focusing on the innovation and improvement of segmentation algorithms and the enhancement of fundus image data.The research contents mainly include:First,summarize the existing diagnosis methods of retinal edema related lesions,introduce the current research status of traditional digital image processing technology in the segmentation of retinal edema-related lesions in fundus retinal images,and the current research status of deep neural network in retinal edema-related lesions segmentation of retinal images.Investigate and sort out the current mainstream scientific research fundus image data set,introduce the data set used in the research,use mainstream digital image processing technology to complete the image preprocessing,and prepare for subsequent model training.Secondly,in view of the current problems in the diagnosis of retinal edema-related lesions of the fundus,based on the summary and analysis of existing diagnostic methods,the semantic segmentation of fundus retinal edema-related lesions based on deep neural network algorithms is studied,and an improved Info GAN-based approach is proposed.Amplification algorithm for fundus OCT image data.The algorithm uses an autoencoder network structure,an improved Res Net-18 network structure is used in the network encoding stage,and the Leaky Re LU activation function is used to solve the problem of network gradient explosion and disappearance;the attention mechanism is introduced in the residual module,and the module is adopted Perform image feature extraction to enrich the feature information of the network in the coding stage.Through this algorithm,the problems of lack of fundus image data and difficulty in obtaining patient images can be effectively improved.Finally,a semantic segmentation algorithm for retinal image edema of fundus based on improved UNet++is proposed.In the network structure,the codec network structure is used,and the image feature information is extracted through the dense expansion convolution module.The module performs the input feature map The continuous convolution operation can better solve the grid effect problem and effectively expand the model’s receptive field.At the same time,add classification branches to the original network structure,and merge the classification branches and segmentation branches through the feature attention module;A novel logarithmic exponential loss function is used in the network to solve the problem of the uneven size of the lesion area in the fundus image.The experimental results show that the improved algorithm proposed in the article has better semantic segmentation performance than the original UNet++network,and the segmentation results have clearer details.It can better complete the semantic segmentation task of fundus images and assist related medical diagnosis.