A hybrid machine learning approach using LBP descriptor and PCA for age-related macular degeneration classification in OCTA images

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%.