Diabetic retinopathy classification with using deep learning

Abstract

This study focuses on developing ensemble deep learning model to classification of diabetic retinopathy (DR) which stands as the most prevalent among microvascular complications associated with diabetes. Even though numerous modeling studies have been conducted for DR classification using various datasets, the differences in fundus photographs influenced by genetic factors may affect the effectiveness of these models, complicating their generalization. In addition to that, thse differences may cause reduced model performance in different regions. Furthermore, given the limited accessibility of health services in developing countries, the signifance of condusting modeling studies on the DR in these regions becomes even more pronounced. In the light of this information, the limited access "Brazilian Multi-Label Opthalmological Data Set" was determined as the reference data set, and all model improvements were carried out based on this data set. DR, macular edema, scar, nevus, drusens, cup disc increase, myopic fundus and age-related macular degeneration, which are listed ad eye abnormalities, are labelled on fundus photographs for the relevant data set. In this study, the AlexNet basis model architecture plays a crucial role as the fundemantal framework for detecting given eyes abnormalities. Subsequently, the outputs of each model are leveraged to refine the DR classification. Using randomly selected data points from the data set, the diagnostic evaluations made by opthalmologists on these fundus photographs are compared with the prediction results of the developed model. The designed model is aimed at improving the decision-making abilities of experienced ophthalmologists in detecting DR. Its main purpose is to reduce possibility of misdiagnosis, which is critical concern in medical evaluations that require high levels of focusing and where even small details creates a valuable insight. By empowering professionals with more robust diagnostic tools and insights, it aims to increase the accuracy and efficiency of diagnoses, ultimately minimizing the occurrence of erroneous conclusions in the classification of DR. Deep learning models were developed using AlexNet architecture, one of the CNN structures, to detect 9 different eye anomalies. Training parameters were tuned by comparing the accuracy and recall metrics of the developed models. The accuracy and recall values of diabetic retinopathy model on test set are 0.76, and 0.73 respectively. In order to improve DR prediction capability, a system has proposed, this system predict the DR class respect to combination of prediction probabilities of the other eye anomalies. A fundus photo is given as input to DR model returns the prediction class if the probability of DR is less than 0.2 or greater than 0.4; otherwise, if the probability of predictind DR is between 0.2 and 0.4, the fundus photo is given as input to other developed models. A final output is produced by weighting the prediction probabilities which is the output of the other anomalies model according to the Pearson correlation coefficient of the relevant anomaly with DR. The proposed method yielded a notable increase in recall for validation set, achieving value of 0.76, which corresponds to an improvement of 8.57%. Similarly, the recall for the test set demonstrated a valuable enhancement, with an observed improvement of 5.2%.