Cataract　is　defined　as　a　lenticular　opacity　presenting　usually　with　poor　visual　acuity.　It　is　one　of　the　most　common　causes　of　visual　impairment　worldwide.　Early　diagnosis　demands　the　expertise　of　trained　healthcare　professionals,　which　may　present　a　barrier　to　early　intervention　due　to　underlying　costs.　To　date,　studies　reported　in　the　literature　utilize　a　single　learning　model　for　retinal　image　classification　in　grading　cataract　severity.　We　present　an　ensemble　learning　based　approach　as　a　means　to　improving　diagnostic　accuracy.　Three　independent　feature　sets,　i.e.,　wavelet-,　sketch-,　and　texture-based　features,　are　extracted　from　each　fundus　image.　For　each　feature　set,　two　base　learning　models,　i.e.,　Support　Vector　Machine　and　Back　Propagation　Neural　Network,　are　built.　Then,　the　ensemble　methods,　majority　voting　and　stacking,　are　investigated　to　combine　the　multiple　base　learning　models　for　final　fundus　image　classification.　Empirical　experiments　are　conducted　for　cataract　detection　(two-class　task,　i.e.,　cataract　or　non-cataractous)　and　cataract　grading　(four-class　task,　i.e.,　non-cataractous,　mild,　moderate　or　severe)　tasks.　The　best　performance　of　the　ensemble　classifier　is　93.2%　and　84.5%　in　terms　of　the　correct　classification　rates　for　cataract　detection　and　grading　tasks,　respectively.　The　results　demonstrate　that　the　ensemble　classifier　outperforms　the　single　learning　model　significantly,　which　also　illustrates　the　effectiveness　of　the　proposed　approach.　(C)　2015　Elsevier　Ireland　Ltd.　All　rights　reserved.