Multiclass Adaptive Boosting Approach for Diabetic Retinopathy Prediction Using Diabetic Retinal Images

Abstract

Scaling up diabetic retinopathy (DR) screening is crucial for preventing blindness caused by this prevalent eye condition, which affects an increasing number of individuals with diabetes worldwide. Early detection of DR and related complications through fundus imaging can effectively halt the progression of the disease to more severe stages. Although recent advancements in convolutional neural network (CNN) techniques have addressed some key challenges in DR screening, the issue of overfitting during the classification process remains due to the limited performance of CNNs in this context. In this study, we propose a novel multiclass adaptive boosting approach to overcome overfitting and enhance classification accuracy. We employ the VGG16 pretrained model for feature learning and the factor analysis method for preprocessing DR images. By integrating the adaptive boosting technique with CNN-based classification, our approach achieves significantly improved accuracy and area under the curve (AUC) scores. This research contributes to the development of more effective and efficient DR screening methods, with the potential to substantially impact diabetes management and patient outcomes.

Labeling Consistency Test of Multi-Label Data for Aspect and Sentiment Classification Using the Cohen Kappa Method

“…There are three classification categories: single-label, multiclass, and multi-label classifications [14,15]. Each category has uses according to the research domain being carried out.…”