Classification of Fluid Filled Abnormalities In Retinal Optical Coherence Tomography Images Using Chicken Swarm Optimized Deep Learning Classifier

Optical Coherence Tomography is a noncontact, non-invasive imaging regarded as the best diagnostic approach when compared to other imaging techniques. It provides convenience along with high resolution and is an indispensable tool utilized for the retinal disease diagnosis in clinical practice. Among retinal diseases, the vision loss is very common caused because of deficit or excess of fluid within the retinal layers. High risk for vision loss occurs due to such damages or deformations in the layers of retina. To tackle this, a deep learning approach is opted for diagnosing fluid filled abnormalities in retinal OCT images. The input image is enhanced by means of CLAHE approach and the retinal layer is segmented for the extraction and selection of relevant features. Gabor filter is adopted for the feature extraction and the selected features are further classified by RNN classifier whose parameters are trained by CSO approach. The utilized RNN provides dynamic system modelling and real-time big data classification. The proposed CSO efficiently trains the RNN parameters with increased accuracy and less time complexity .The NEH OCT image dataset is used in MATLAB to validate the proposed approach. The obtained results demonstrate the efficacy of the proposed approach with an accuracy of 95% and reduced processing time of 545.4s.