**3. Result**

The new proposed procedure was realized for 114 Images which were obtained from Topcon and Zeiss OCT gadgets. Among the accessible dataset of 27 Normal Images and 87 Abnormal Images, in absolute 70 level of the pictures were used for preparing and 30 rate where utilized for testing and validations. Right off the bat the

**Figure 2.** *Input image.*

picture changes over gray scale and exposed to preprocessing utilizing various filters for evacuation of speckles. While the original input image and gray level image could be seen in **Figures 2** and **3** respectively, the outputs of various filters evaluated is shown below in **Figure 4**.

When most of the filters just average the data instead of removing the speckles, the computational results show that Homomorphic wiener filter is comparatively better, as the filter is converting the multiplicative noises into additive and hence noise removal is much efficient.

Preprocessed images are exposed to segmentation utilizing Gradient Vector Flow calculation. From the segmented image, different highlights like, Skewness, Entropy, Energy, and Variance were extricated and these highlights spoke to have a critical striking distinction among the ordinary and anomalous pictures. With these highlights as features to the classifiers, the data is being presented to the random forest network classifier whose various statistical values are being tabulated in **Tables 1** and **2**. The various parameters of statistics include True Positives, True Negatives, Untrue Positives and Untrue Negatives, Specificity, Accuracy, and Sensitivity.

From the general outcomes it is reasonable that Random Forest classifier is by all accounts equally more hopeful than different classifiers. These results seem to be similarly efficient and comparable with the previous related works.

**Figure 3.** *Grayscale image.*

*Study and Analysis of Fluid Filled Abnormalities in Retina Using OCT Images DOI: http://dx.doi.org/10.5772/intechopen.109646*

#### **Figure 4.**

*Output of various speckle reduction filters. (a) Mean filter 3\*3, (b) mean filter 5\*5, (c) mean filter 7\*7, (d) frost filter, (e) adaptive smoothing filters, (f) Gaussian filters 3\*3, (g) Gaussian filters 5\*5, (h) Gaussian filters 7\*7, (i) bilateral filters, (j) anisotropic diffusion filters, (k) homomorphic wiener filters 3\*3, (l) homomorphic wiener filters 5\*5.*


#### **Table 1.**

*Performance output of random Forest classifier.*


#### **Table 2.**

*Performance analysis of overall system.*

## **4. Discussion**

The Random Forest classifier seems to be comparatively efficient which is shown in the obtained results, when compared to the other classifiers, comparing to the earlier related works [25–27], the proposed system seems to be equally efficient towards classifying the input image as abnormal and normal. The overall system parameters like specificity, sensitivity, and accuracy seems to be more hopeful when compared with the existing methodologies as cited. By expanding the range of abnormalities and also identifying age related disorders can be further enhanced.

### **5. Conclusion**

The proposed framework, and calculations of arrangement, is by all accounts proficient. The degree of precision accomplished demonstrates that the framework can likewise be used for useful ramifications. The effectiveness of the proposed classifier frameworks should be assessed for explicit variation from the norm-based arrangement like Cystoid Macular Edema, Choroidal Neo Vascular Membrane, Epi Retinal Membrane, Macular Hole, Age related Macular Degeneration in future, in view of the real execution could be precisely examined in detail. On definite issuebased order study, the created framework can likewise be coordinated with the OCT gadgets progressively to consequently remark on the pictures that are given as the contribution to the framework.

The developed system was implemented and evaluated with MATLAB and the validation of the system was done with reference to Ophthalmologists. The comments of the ophthalmologists on the proposed system is attached. Though the overall accuracy of the system matches the earlier works, significant improvisation could be seen with the number of abnormalities that had been included in the process of classification. With overall system accuracy of around 91.65% the proposed system is significantly in match with the existing systems. The accuracy, sensitivity, specificity and Youden's index are acceptable for the system proposed and has been cross validated for its performance by Ophthalmologists and found to be satisfactory. The obtained results in terms of accuracy of 91.65% are closer with the related works whose accuracies are closer to the values obtained by the proposed system Reza Rasti et al. [16]. The obtained performance metrics shows that the acquired accuracy is remarkably higher than the works of Philipp Seebock et al. of 81.4%, which focused to limited abnormalities. The comparison could be understood from the representation in **Table 3**.

Earlier works focused on morphological parameters alone which restricted the number of disorders taken into consideration for categorization. Apart from the morphological parameters of bulkiness, compactness and convexity, the evaluated system also used Zernike moments which is widely varying feature for the disorders


**Table 3.** *Comparison of performance.*
