A CNN Approach to Central Retinal Vein Occlusion Detection
Abstract
In the field of medical there is a need for automatic detection of retinal disorders. Central Retinal Vein Occlusion (CRVO) is the root cause of blindness occurred in the old age people. It results in rapid, irreversible eyesight loss ,therefore, It is essential to identify and address CRVO as soon as feasible.Hemorrhages, which can differ in size, pigment, and shape from dot-shaped to flame hemmorages, are one of the earliest symptoms of CRVO. The early signs of CRVO are, hemmorhages,however, so mild that ophthalmologists must dynamically observe such indicators in the retina image known as fundus image, which is a challenging and time-consuming task. It is also difficult to segment hemorrhages since the blood vessels and hemorrhages has same color properties and also there is no particular shape for hemorrhages and it scatters all over the fundus image. There are automatic detection techniques for eye illness diagnosis, but their effectiveness is dependent on a number of parameters. A challenging mathematical study is needed to extract the characteristics of vein deformability and dilatation. Furthermore, the quality of the captured image affects the efficacy of feature Identification analysis. This paper has proposed deep learning approach for the extraction of hemorrhages.
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