  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.
