Effects of Filters in Retinal Disease Detection on Optical Coherence Tomography (OCT) Images Using Machine Learning Classifiers

Asad  Wali; Arjun  Sipani

Authors

Asad Wali Department of Computer Science, Punjab University College of Information Technology (PUCIT), Lahore, Pakistan
Arjun Sipani William B Travis HS, Richmond, TX, United States

Keywords:

Random Forest Classifier (RFC), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Machine Learning, Optical Coherence Tomography (OCT), Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), DRUSEN, NORMAL., Diabetic Retinopathy (DR), Age Related Macular Degeneration (AMD), Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Features from Opponent Space for Filtering (FOSF)

Abstract

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 DME, CNV, and DRUSEN. Despite its importance, there is a pressing need to enhance the 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 generates models for detecting retinal disease on a publicly available dataset of retinal OCT images using machine learning classifiers with the help of image feature extractions. It classifies the given retinal OCT images as DME, CNV, DRUSEN, and NORMAL. Firstly, it extracts image features using appropriate methods and then it is trained, after training it passes through machine learning classifiers to classify the given input images, and then it is tested to get a 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 HOG, LBP, and FOSF. This comparative analysis serves to assess the efficacy of finding the best approach in relation to these widely recognized methods. The proposed experiments based on these approaches reveal that the use of HOG on this dataset outperforms with SVM classifier with a maximum accuracy of 78.8%.

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