Ensemble learning-based method for multiple sclerosis screening from retinal OCT images.

Multiple sclerosis (MS) is a neurodegenerative disease that impacts retinal layer thickness. Thus, several works proposed to diagnose MS from the retinal optical coherence tomography (OCT) images. Recent clinical studies affirmed that thinning occurs on the four top layers, explicitly in the macular region. However, existing MS detection methods have not considered all MS symptoms, which may impact the MS detection performance. In this research, we propose a new automated method to detect MS from the retinal OCT images. The main principle is based on extracting the relevant retinal layers and figuring out the layer thicknesses, which are investigated to deduce the MS disease. The main challenge is to guarantee a higher performance biomarker extraction within an efficient exploration of OCT cuts. Our contribution consists of the following: (1) employing two DL architectures to segment separately sub-images based on their morphology, in order to enhance segmentation quality; (2) extracting thickness features from the four top layers; (3) dedicating a classifier for each OCT cut that is selected based on its position with respect to the macula center; and (4) merging the classifier knowledge through an ensemble learning approach. Our suggested method achieved 97% accuracy, 100% sensitivity, and 94% precision and specificity, which outperforms several state-of-the-art methods.