  This paper experiments with the number of fully-connected layers in a deep
convolutional neural network as applied to the classification of fundus retinal
images. The images analysed corresponded to the ODIR 2019 (Peking University
International Competition on Ocular Disease Intelligent Recognition) [9], which
included images of various eye diseases (cataract, glaucoma, myopia, diabetic
retinopathy, age-related macular degeneration (AMD), hypertension) as well as
normal cases. This work focused on the classification of Normal, Cataract, AMD
and Myopia. The feature extraction (convolutional) part of the neural network
is kept the same while the feature mapping (linear) part of the network is
changed. Different data sets are also explored on these neural nets. Each data
set differs from another by the number of classes it has. This paper hence aims
to find the relationship between number of classes and number of
fully-connected layers. It was found out that the effect of increasing the
number of fully-connected layers of a neural networks depends on the type of
data set being used. For simple, linearly separable data sets, addition of
fully-connected layer is something that should be explored and that could
result in better training accuracy, but a direct correlation was not found.
However as complexity of the data set goes up(more overlapping classes),
increasing the number of fully-connected layers causes the neural network to
stop learning. This phenomenon happens quicker the more complex the data set
is.
