In　parallel　with　the　development　of　various　emerging　fields　such　as　computer　vision　and　related　technologies,　e.g.,　iris　identification　and　glaucoma　detection,　criminals　are　developing　their　methods.　It　is　the　foremost　reason　for　the　blindness　of　human　beings　that　affects　the　eye＇s　optic　nerve.　Fundus　photography　is　carried　out　to　examine　this　eye　disease.　Medical　experts　evaluate　fundus　photographs,　which　is　a　time-consuming　visual　inspection.　Most　current　systems　for　automated　glaucoma　detection　in　fundus　images　rely　on　segmentation-based　features　nuanced　by　the　underlying　segmentation　methods.　Convolutional　neural　networks　(CNNs)　are　powerful　tools　for　solving　image　classification　tasks,　as　they　can　learn　highly　discriminative　features　from　raw　pixel　intensities.　However,　their　applicability　to　medical　image　analysis　is　limited　by　the　nonavailability　of　large　sets　of　annotated　data　required　for　training.　In　this　work,　we　aim　to　accelerate　this　process　using　a　computer-aided　diagnosis　of　this　severe　disease　with　the　help　of　transfer　learning　based　on　deep　convolutional　neural　networks.　We　have　suggested　the　Inception　V-3　approach　for　image　classification　based　on　convolution　neural　networks.　Our　developed　model　has　the　potential　to　address　this　CNN　model＇s　problem　of　classification　accuracy,　and　with　imaging　data,　our　proposed　method　outperforms　recent　state-of-the-art　approaches.　The　case　study　for　digital　forensics　is　an　essential　component　of　emerging　technologies,　and　hence　glaucoma　detection　plays　a　vital　role　in　it.