The cross-sectional views of retinal tissues are obtained from this imaging modality. Optical Coherent Tomography (OCT) is a non-invasive technique capturing retinal tissue images. Automatic retrieval of retinal OCT volumes for the presence of a particular ailment can help ophthalmologists in the mass screening process. Per image mean average retrieval time is 8.3 sec. Experiments show that the method is noticeably successful in retrieving similar OCT volumes. Considering all possible variations of retrieval, we achieved overall mean average precision and mean reciprocal rank of 0.631167 and 0.829607, respectively which are also quite notable with rank thresholds of 3, 5 and 7. The system retrieves similar scans from a dataset of abnormal and normal OCT scans with a mean average precision of 0.7571 and mean reciprocal rank of 0.9050. Execution time optimization has also been achieved as the network used is comparatively shallow and network training is not required. We evaluated different variations of retrieval performances like AMD-Normal, DME-Normal, AMD-DME, AMD-DME-Normal, etc. The system successfully retrieves retinas with similar symptoms from the database of differently affected and unaffected OCT scans. These requirements have been eliminated automatically as part of the Twin network implementation procedure. Most of the techniques involving deep network implementation suffer from the drawbacks of data augmentation and resizing. The Twin network comparison approach exploits deep features without the resource, space and computation exhaustive network training phase. In this work, we propose a retrieval system for retinal OCT scans which extracts feature maps of both query and database samples from the layer of deep convolutional neural network and compares for their similarity. As per the literature survey, till date, no papers are there which deal with the retrieval of retinal OCT scans. A number of existing methods take care of segmentation and identification of retinal landmarks and pathologies from OCT volumes. Content based retinal OCT scan retrieval process makes use of characteristic features to retrieve similar Optical Coherent Tomography (OCT) scans, index-wise, from a database with minimal human intervention. Among these abnormalities, Diabetic Macular Edema (DME) and Age Related Macular Degeneration (AMD), both are frequent retinal degenerative diseases leading to blindness. Retinal imaging helps to detect retinal and cardiovascular abnormalities.
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