Artificial Intelligence (AI) Enabled Image Upscaler for Retinal Anomaly Detection with Dense Neural Computation
Keywords:
Retinal diseases, Artificial intelligence, Deep learning, Neural computing, Image segmentationAbstract
Retinal abnormality is a kind of chronic impact developed due to continuous accumulation of fluid in the retinal space. Untreated retinal infection leads to permanent damage to the organ. The segmentation of retinal cyst from the optical coherence tomography (OCT) images crucial to identify the disease in the early stages. The quality of OCT image is crucial to determine the infected area accurately. Most of the data collected from screening labs contains unstructured OCT images with and without labels. The processing time taken for handling the clogged image pixels are high. It degrade the performance of prediction system. Dropping out of low quality image is important instead of utilizing the raw data for prediction process. The segmentation of infected area is utilized to classify the type of retinal disease such as Choroidal vascularization, muscular Edema, Drusen and normal images. The proposed system is framed in such a way to create enhanced screening images through artificial intelligence (AI) enabled image upscale (AIU) using Zyro tool. The up scaled images are further utilized for feature extraction process towards deep identification of unique impacts in the OCT images. The classification is explored with deep dense neural computing (DDNC) through deep neural network (DNN). The proposed AI upscale deep dense network (AIU-DDNC) classify the feature vectors with respect to disease types of trained vectors within the same network. The RETOUCH dataset is utilized here for creating a standard model and further the presented system achieved 98.89% accuracy on retinal disease classification is compared with existing state of art approaches..
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