**2. Methods and materials**

The overall flow diagram of the entire methodology could be clearly understood from **Figure 1**.

Images are acquired using SD OCT Device in digital format and provided as input to our system. Image inputs are exposed to preprocessing which includes grayscale conversion and noise removal. OCT practices ultrasound as a wellspring of imaging, the pictures are increasingly inclined to spot commotions, in nature these are multiplicative [16–20]. So as to dispense with/limit these commotions, homomorphic channels were utilized to kill the spot clamors. In spite of the fact that different channels are available. In the wake of preprocessing, so as to seclude the retinal layers, various methodology of division [19–22] is listed out. This assurance the retinal layers of intrigue remain disengaged from the repetitive foundation for additional handling of the image. This dynamic division calculation, the Gradient Vector stream calculation of division in the framework of projected and assessed. The division calculation figures the dissemination of slope vectors of dim level or paired edge map which is gotten from the info preprocessed picture. Therefore, the yield is normal give an unmistakable perspective on the obligatory retinal layers with liquid founded irregularities as opposed to considering the whole picture information for further preparing.

Different highlights of the divided picture were extricated among which they chose highlights are Skewness, Entropy, Variance and Energy which are the factual highlights which are utilized as info information in characterization of the pictures as typical and strange. Critical contrasts could be found in the highlights of ordinary and unusual pictures and consequently these are utilized in further arrangement. (A variance image, an image of variances that is the squares of the standard deviations, of input or output images). If a variance set is delivered, all pixel values of the variance image must be the pixels of inset which may have any value.

The separated highlights are part for preparing and testing/approval of the classifiers in a proportion of 3:1 for accomplishing a superior generally speaking *Study and Analysis of Fluid Filled Abnormalities in Retina Using OCT Images DOI: http://dx.doi.org/10.5772/intechopen.109646*

execution of the classifiers. The highlights are exhibited to Random Forest Classifier so as to get the yield in paired structure either as Normal or Abnormal. Random Forest Classifier assesses the information include into various classes and plays out a democratic check to decide the most extreme likelihood in having a place of the information highlight vector. The classifier checks for the lion's share casting a ballot under each woodland and characterizes the info occurrence dependent on the most extreme votes. Random forest includes of a huge number of discrete choice trees that work as a congregation.

Endless supply of the component information, above all clarified classifiers, the yields of the equivalent are arranged to recognize untrue positive, True negative, genuine positive and untrue negative [23, 24]. From these qualities' affectability, explicitness and thus the general framework precision/execution is being assessed for every one of the classifiers independently and this outcome is utilized to recognize the best classifier among the proposed calculations.
