AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Autori: MARCELL DÁVID TÓTH, ATTILA KISS.
DOI: 10.24193/subbi.2021.1.05
Published Online: 2021-06-30
Published Print: 2021-06-30
pp. 74-85
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The average human lifespan increased dramatically in the second half of 20th century. It was mainly due to technological improvements, which were driven by the continuous war preparations, and while humans have got another 20 years to live, unfortunately there are some sad side effects added to the elderly life. Various diseases can attack the eye, our major organ responsible for receiving information, therefore many researches were devoted to examine these diseases, their early signs, and how could they be stopped. From the start of 21th century, methods aided by computer were more and more involved in these processes, up to the current trend of using Convolutional Neural Networks (CNNs). While supervised methods, CNNs do achieve accuracy which can be compared to a skilled ophthalmologist, they require a tremendous amount of labeled data which is sparse in medical fields because the amount of time and resources needed to create them. One natural solution is to augment the data present, that is, copying the distribution while adding a small variety, like coloring an image differently. That is, what our paper aims to explore, whether a texturing algorithm, the Neural Style Transfery can be used to make a data set richer, and therefore helping a classifier CNN to achieve better results.
Keywords and phrases: Medical Image Processing, Retinal Blood Vessel Segmentation, Neural Style Transfer.
2010 Mathematics Subject Classification: 68U10, 68T01, 92B20.
1998 CR Categories and Descriptors. I.4.6 [I.2 Image Processing and Computer Vision]: Segmentation - Region, partitioning; I.5.1 [Pattern Recognition]: Models - Neural Nets.
