Cataract　is　one　of　the　prevailing　cause　of　blindness　in　the　industrial　world　that　accounts　for　more　than　50%　of　blindness.　The　early　detection　of　cataract　can　protect　serious　threats　of　visual　impairment.　Most　of　the　existing　work　is　based　on　manual　extraction　of　features,　but　this　paper　aims　at　automatic　detection　of　a　cataract　into　its　different　grades　using　deep　convolutional　neural　network　integrated　with　data　augmentation　techniques.　The　Gaussian-scale　space　theory　and　the　general　data　augmentation　settings　are　used　to　improve　the　dataset　in　terms　of　quality　and　quantity,　which　lead　to　overcome　the　issues　of　the　unbalanced　dataset.　The　training　and　testing　of　the　proposed　model　are　performed　on　both　the　original　dataset　and　the　augmented　dataset.　The　model　accuracy　of　the　convolutional　neural　network　with　augmented　dataset　presented　in　this　paper　is　0.9691,　which　shows　an　optimal　performance　compared　with　the　original　dataset,　and　other　methods.　©　2020,　Springer　Nature　Singapore　Pte　Ltd.