The Neural Network of One-Dimensional Convolution-An Example of the Diagnosis of Diabetic Retinopathy

Diabetes is a serious threat to health development, because diabetes is a disease that caused most other diseases (complications). Diabetic retinopathy is the most important manifestation of diabetic microangiopathy and is also one of the most common complications in people with diabetes. At present, the diagnosis of diabetic retinal complications mainly depends on the pictures for diagnosis. The fundus images are the main ways to diagnose retinal diseases at present, but the diagnosis process is complicated. Based on this, this paper uses the electronic medical record information of 301 hospitalized patients with diabetes from 2009 to 2013, mainly using the diabetes diagnostic data, diabetes glycosylation data and diabetes biochemical test data, the depth of learning methods and medical diabetes combined with the application of convolution Neural Network Method (CNN) to build a diagnostic model, and thus draw the diagnosis. The main contribution of this study is twofold: 1) In this paper, we apply the CNN method to one-dimensional unrelated data sets and solve the problem of how to do one-dimensional irrelevant data convolution. 2) In this paper, the CNN model is combined with the BN layer to prevent the dispersion of the gradient, speed up the training speed and improve the accuracy of the model. In addition, this model incorporates an adaptive learning rate algorithm and optimizes the model. The experiments show that this method can achieve a training accuracy of 99.85% and a testing accuracy of 97.56%, which is more than 2% higher than that of using logistic regression. The model methods involved in this study can not only be used for the diagnosis of diabetic retinopathy, but also for the diagnosis of other diseases, such as chronic kidney disease, cardiovascular, and cerebrovascular diseases.