Cataract　is　the　most　prevalent　cause　of　blindness　worldwide,　which　accounts　for　more　than　51%　of　overall　blindness.　The　early　detection　of　cataract　can　salvage　impaired　vision　leading　to　blindness.　Most　of　the　existing　cataract　classification　systems　are　based　on　traditional　machine　learning　methods　with　hand-engineered　features.　The　manual　extraction　of　retinal　features　is　generally　a　time-taking　process　and　requires　professional　ophthalmologists.　Convolutional　neural　network　(CNN)　is　a　widely　accepted　model　for　automatic　feature　extraction,　but　it　necessitates　a　larger　dataset　to　evade　overfitting　problems.　Contrarily,　classification　using　SVM　has　great　generalisation　power　to　elucidate　small-sample　dataset.　Therefore,　we　proposed　a　hybrid　model　by　integrating　deep　learning　models　and　SVM　for　4-class　cataract　classification.　The　transfer　learning-based　models　(AlexNet,　VGGNet,　ResNet)　are　employed　for　automatic　feature　extraction　and　SVM　performs　as　a　recogniser.　The　proposed　architecture　evaluated　on　8030　retinal　images　with　strong　feature　extraction　and　classification　techniques　has　achieved　95.65%　of　accuracy.　The　results　of　this　study　have　verified　that　the　proposed　method　outperforms　conventional　methods　and　can　provide　a　reference　for　other　retinal　diseases.