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Feature Extraction of Ophthalmic Images using Deep Learning and Machine Learning Algorithms.
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Academic Editor: Alessandro Bruno


Deep learning and Machine Learning Algorithms has become the most popular way for analyzing and extracting features especially in medical images. And feature extraction made the task much easier. Our aim is to check which feature extraction technique works best for a classifier. We used Ophthalmic Images and applied feature extraction techniques such as Gabor, LBP (Local Binary Pattern), HOG (Histograms of Oriented Gradients), and SIFT (Scale-Invariant Feature Transform), where the obtained feature extraction techniques are passed through classifiers such as RFC (Random Forest classifier), CNN (Convolutional neural network), SVM (Support vector machine), and KNN (K-Nearest Neighbors). Then we compared the performance of each technique and selected which feature extraction technique gives the best performance for a specified classifier. We achieved 94% accuracy for Gabor Feature Extraction technique using CNN Classifier, 92% accuracy for HOG Feature Extraction technique using RFC Classifier, 90% accuracy for LBP Feature Extraction technique using RFC Classifier and we achieved 92% accuracy for SIFT Feature Extraction technique using RFC Classifier.

Keywords: Ophthalmic images; Diabetic Retinopathy; Feature Extraction; CNN; SVM; KNN; RFC; LBP; GABOR; HOG; SIFT.