Effects of Filters inRetinal Disease Detection onOptical Coherence Tomography (OCT) ImagesUsing Machine Learning Classifiers

Optical Coherence Tomography (OCT) is an essential, non-invasive imaging technique for producing high-resolution images of the retina, crucial in diagnosing and monitoring retinal conditions such as diabetic macular edema (DME), choroidal neovascularization (CNV), and DRUSEN. Despite its importance, thereis a pressing need to enhance early detection and treatment of these common eye diseases. While deep learning methods have shown higher accuracy in classifying OCT images, the potential for machine learning approaches, particularly in terms of data size and computational efficiency, remains underexplored. This study presentsdifferent experiments for detect the retinal disease on publically available dataset of retinal optical coherence tomography (OCT) images using machine learning classifiers with the help of image feature extractions. It classifies the given retinal OCT images as diabetic macular edema (DME), choroidal neovascularization (CNV), DRUSEN and NORMAL. Firstly, it extracts image features using appropriate methods and then it is trained, after training it pass through machine learning classifiers to classify the given input images and then it is tested to get the better accuracy performance. The above steps are iterated by varying over the pre-processing techniques in which we first resize the image into 100 x 100 after resizing, we remove the noise by using Gaussian Blur and then normalize the image. We systematically benchmark its performance against established built-in methods, such as Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Feature fromOpponent Space for Filtering (FOSF). This comparative analysis serves to assess the efficacy of to find out the best approach in relation to these widely recognized methods. The proposed experiments based on these approaches reveals that the use of HOG onthis dataset outperform with SVM classifier with maximum accuracy of 78.8%.