**1. Introduction**

Biomedical Imaging and automations in medicine has become an emerging field due to the need of precision, accuracy and storage/retrieval capability of the data being handled. Automations in the field of ophthalmology has also proven to be an emerging need of the hour as more accurate diagnosis of small changes in the retinal layers shall prevent the vision impairment. Such variations are commonly initiated owing the alterations in fluid pattern of the retinal coverings. The fluid may become excess or deficit based on the specified abnormality and due to this the patient may suffer a loss of vision. In order to identify this fluid pattern more accurately ultrasound-based imaging modality, namely, developing imaging techniques such as Optical Coherence Tomography (OCT) Ghastly Domain Optical Coherence Tomography (SD-OCT) mentioned in [1], which unmistakably differentiates numerous infections in different layers of the retina [2]. An inner layer of an eye is Retina which changes over the occurrence bright flag into neural sign, which are conveyed to the mind. It comprises of different shades, poles and cones are in charge of blurred

light and shading dreams individually. Retina has a few layers of neurotic and physiological significance. Some harms in the retina layers lead to few more hazard variations from the norm including vision loss. Prior examination of OCT Images focused on separation of Intraretinal layers [3–12], division of fluid engaged layers, [13, 14] and optical circle is a fundamental one.

Medical Image Processing classification assumes a remarkable work towards familiar proof and outcome of variations are observed from the model in different imaging modalities. OCT employs ultrasound waves as a source and transceives the corresponding pictures of different layers of retina [15]. OCT is utilized in 3D reconstructible picture of retina which is required for better comprehension of real investigation, liquid-based variation from the norm. Priori stages to visual deficiency incorporate modifications in the liquid example of the different retinal layers. Scarcely any such anomalies are cystoids macular edema (CME), wherein the macular layer gets gathered with abundance of liquids, Choroidal Neo Vascular Membrane (CNVM), a fresh recruits' vessels are framed and liquid substance winds up more in the Choroidal layer, and Macular Hole (MH), wherein the macular layer progresses towards becoming shortfall of liquids. Various methodologies and implementation of the proposed system is explained in the below section.
