A Comparison of Handcrafted and Deep Neural Network Feature Extraction for Classifying Optical Coherence Tomography (OCT) Images

TL;DR

This study compares handcrafted feature extraction methods with deep neural networks for classifying OCT eye images, demonstrating that deep learning significantly outperforms traditional techniques in accuracy and class representation.

Contribution

The paper provides a comprehensive comparison between handcrafted features and deep neural network features for OCT image classification, highlighting the superior performance of deep learning methods.

Findings

Deep neural networks achieved around 88-89% accuracy.

Handcrafted features achieved around 42-50% accuracy.

Deep learning methods performed better on underrepresented classes.

Abstract

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.