Abstract
In the medical imaging, early and precise segmentation of retina blood vessel (RBV) has been considered as one of the most key factors to diagnose the ophthalmologic diseases such as diabetic retinopathy, hypertension, arteriosclerosis, cardiovascular disease, and age-related macular degeneration. However, owing to very complex anatomy of the fundus, manual segmentation has been found as troublesome and tedious task along with lots of required knowledge and skills. Therefore, in the proposed work incorporating autoencoders has been proposed. To investigate the effectiveness of proposed methodology, DRIVE dataset has been employed. Further, the available dataset images have been converted into patches of 10 × 10 to encounter the very small size of utilized dataset. The developed model achieves more than 90% classification accuracy in most of the cases which validates the effectiveness of the proposed methodology.
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Varshney, H., Kant, U., Gupta, H., Verma, O.P., Sharma, T.K., Ansari, I.A. (2021). Semantic Segmentation of Retinal Blood Vessel with Autoencoders. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1381. Springer, Singapore. https://doi.org/10.1007/978-981-16-1696-9_53
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