Authors :
Revani Naik; Arpitha C. N.; Chaithra I. V.; Sangareddy B Kurtakoti
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://tinyurl.com/2fapcryb
Scribd :
https://tinyurl.com/49wucr4h
DOI :
https://doi.org/10.5281/zenodo.10153576
Abstract :
Central Serous Retinopathy (CSR) is a retinal
disease that results in blindness and visual loss. The
accumulation of watery fluid behind the retina causes
the CSR. Detection and prevention of CSR disease is
desirable, it helpsto take preventive measures to avoid
and overcome any damages to the human eye. For the
purpose of detecting CSR disease and analyzing the
results 2 imaging approaches are used. Optical
Coherence Tomography Angiography (OCT), Fundus
Imaging are the two imaging (dataset) techniques used in
this work. Before classification of the input dataset pre-
preparation of the image dataset plays an important role
classification using machine learning methods. Image
processing increases the accuracy in detection of disease.
The preprocessing stage in our proposed system consists
of four main phases, namely noise removal, gray-scale
conversion, median filtering, and data transformation.
Data transformation in the proposed system consists of
five image transformation steps such as random
horizontal flip, random rotation, random resizing,
transforming to tensor and normalizing the data.
Keywords :
Central Serous Retinopathy (CSR), OCT and Fundus imaging.
Central Serous Retinopathy (CSR) is a retinal
disease that results in blindness and visual loss. The
accumulation of watery fluid behind the retina causes
the CSR. Detection and prevention of CSR disease is
desirable, it helpsto take preventive measures to avoid
and overcome any damages to the human eye. For the
purpose of detecting CSR disease and analyzing the
results 2 imaging approaches are used. Optical
Coherence Tomography Angiography (OCT), Fundus
Imaging are the two imaging (dataset) techniques used in
this work. Before classification of the input dataset pre-
preparation of the image dataset plays an important role
classification using machine learning methods. Image
processing increases the accuracy in detection of disease.
The preprocessing stage in our proposed system consists
of four main phases, namely noise removal, gray-scale
conversion, median filtering, and data transformation.
Data transformation in the proposed system consists of
five image transformation steps such as random
horizontal flip, random rotation, random resizing,
transforming to tensor and normalizing the data.
Keywords :
Central Serous Retinopathy (CSR), OCT and Fundus imaging.
