Ensemble learning based method for severity detection of Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a leading cause of vision impairment and blindness among elderly people. AMD disease has different severity grades requiring different treatment procedures. Many studies proposed automated methods grading AMD using color fundus images, but none achieved optimal performance. In this study we aim to develop an automated method to detect the severity of AMD from color fundus images. The main contribution consists of a stacking ensemble learning approach, which combines the knowledge of five CNN models in order to perform accurate AMD severity grading. Experimental results show that the ensemble method achieves a classification accuracy of 95.2%, a precision of 95.27, a sensitivity of 95.25%, a specificity of 96.68% and an F1-score of 95.11% which outperforms several existing methods.