**Abstract**

Visual impairment is one of the most regularly happening infections in human. The reason being variation from the normal in the different layers of retina because of strange measure of liquid either abundance aggregation or shortage. This paper targets recognizing and assessing the different abnormalities that could be earlier stages to visual deficiency. The proposed target is achieved by means of implementation using Digital Image Processing Technique, starting from preprocessing to classification at various stages. Not restricting to binary classification as normal or abnormal, the proposed system also extends its capacity to classify the input image as Cystoid Macular Edema (CME), Choroidal Neo Vascular Membrane (CNVM), Macular Hole (MH) and normal images. The preprocessing methodology implemented filters to remove the speckle noises which are most common in ultrasound-based imaging system. Random forest classifier was utilized for classifying the input features and also seems to be promising on par with the various existing methodologies.

**Keywords:** classifiers, ophthalmic imaging, optical coherence tomography, retinal disorders, fluid filled abnormalities
