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This paper experiments with the number of fully-connected layers in a deepconvolutional neural network as applied to the classification of fundus retinalimages. The images analysed corresponded to the ODIR 2019 (Peking UniversityInternational Competition on Ocular Disease Intelligent Recognition) [9], whichincluded images of various eye diseases (cataract, glaucoma, myopia, diabeticretinopathy, age-related macular degeneration (AMD), hypertension) as well asnormal cases. This work focused on the classification of Normal, Cataract, AMDand Myopia. The feature extraction (convolutional) part of the neural networkis kept the same while the feature mapping (linear) part of the network ischanged. Different data sets are also explored on these neural nets. Each dataset differs from another by the number of classes it has. This paper hence aimsto find the relationship between number of classes and number offully-connected layers. It was found out that the effect of increasing thenumber of fully-connected layers of a neural networks depends on the type ofdata set being used. For simple, linearly separable data sets, addition offully-connected layer is something that should be explored and that couldresult 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 tostop learning. This phenomenon happens quicker the more complex the data setis.

The relationship between Fully Connected Layers and number of classes for the analysis of retinal images