Predictive Analysis of Diabetic Retinopathy with Transfer Learning

摘要

With the prevalence of diabetes, Diabetes Mellitus Retinopathy (DR) is becoming a major health problem across the world. The long-term medical complications arising due to DR significantly affect the patient as well as the society, as the disease mostly affects young and productive individuals. Early detection and treatment can help reduce the extent of damage to the patients. The rise of Convolutional Neural Networks for predictive analysis in the medical field paves the way for a robust solution to DR detection. This paper studies the performance of several highly efficient and scalable CNN architectures for Diabetic Retinopathy Classification with the help of Transfer Learning. The research focuses on VGG16, Resnet50 V2, and EfficientNet B0 models. The classification performance is analyzed using several performance measures including True Positive Rate, False Positive Rate, Accuracy, etc. Also, several performance graphs are plotted for visualizing the architecture performance including Confusion Matrix, ROC Curve, etc. The results indicate that Transfer Learning with ImageNet weights using VGG 16 model demonstrates the best classification performance with an accuracy of 95%. It is closely followed by ResNet50 V2 architecture with an accuracy of 93%. This paper shows that predictive analysis of DR from retinal images is achieved with Transfer Learning on Convolutional Neural Networks.