Multi-label learning model for improving retinal image classification in diabetic retinopathy

Retinal image analysis may disclose severity and causes of many diabetic diseases e.g. for Diabetic Macular Edema inspection. Many techniques have been introduced for automatic classification of exudate lesion to speed up the diagnosis of diabetic disease. In almost all previous work, exudate lesion detection is either modelled as binary or multiclass classification problem. However, along with the classification of normal / abnormal regions, other information needs to be simultaneously classified such as patient's age, ethnicity, race, diabetic's type etc. In this work, we presented a new technique, namely Multi-label learning model to improve the classification of exudate lesions. Features are extracted using multi-scale local binary patterns. Multi label k nearest neighbour (ML-kNN), Multi-label Ranking Support Vector Machine Learning (ML-Rank SVM), Multi-label Learning Neural Network Radial Base Function (MLNN-RBF) and Multi-label Learning Neural Network Back-Propagation (MLNN-BP) are evaluated as the classification models and compared with traditional binary multi-class classifiers. Experiment results show that multi-label framework is very useful for diabetic retinopathy differentiation and can improve retinal image classification.