Abstract
We propose a novel hybrid machine learning approach for age-related macular degeneration (AMD) classification to support the automated analysis of images captured by optical coherence tomography angiography (OCTA). The algorithm uses a Rotation Invariant Uniform Local Binary Patterns (LBP) descriptor to capture local texture patterns associated with AMD and Principal Component Analysis (PCA) to decorrelate texture features. The analysis is performed on the entire image without targeting any particular area. The study focuses on four distinct groups, namely, healthy; neovascular AMD (an advanced stage of AMD associated with choroidal neovascularisation (CNV)); nonneovascular AMD (AMD without the presence of CNV) and secondary CNV (CNV due to retinal pathology other than AMD). Validation sets were created using a Stratified K-Folds Cross-Validation strategy for limiting the overfitting problem. The overall performance was estimated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC). The classification was conducted as a binary classification problem. The best performance achieved with the SVM classifier based on the AUC score for: (i) healthy vs neovascular AMD was 100%, (ii) neovascular AMD vs non-neovascular AMD was 85%; (iii) CNV (neovascular AMD plus secondary CNV) vs non-neovascular AMD was 83%.