A Comparison of Handcrafted and Deep Neural Network Feature Extraction for Classifying Optical Coherence Tomography (OCT) Images
This incremental work addresses disease detection in ophthalmology by evaluating feature extraction methods for OCT image classification.
The study compared handcrafted features (HOG, LBP) and deep neural network features (DenseNet-169, ResNet50) for classifying OCT images into four classes, finding that deep methods achieved 88-89% accuracy, significantly outperforming handcrafted methods at 42-50%.
Optical Coherence Tomography allows ophthalmologist to obtain cross-section imaging of eye retina. Assisted with digital image analysis methods, effective disease detection could be performed. Various methods exist to extract feature from OCT images. The proposed study aims to compare the effectiveness of handcrafted and deep neural network features. The evaluated dataset consist of 32339 instances distributed in four classes, namely CNV, DME, DRUSEN, and NORMAL. The methods are Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP), DenseNet-169, and ResNet50. As a result, the deep neural network based methods outperformed the handcrafted feature with 88% and 89% accuracy for DenseNet and ResNet compared to 50 % and 42 % for HOG and LBP respectively. The deep neural network based methods also demonstrated better result on the under represented class.