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

by   Ajna Ram, et al.

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.



There are no comments yet.


page 2

page 3

page 4

page 6


Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection

In this paper, we evaluate convolutional neural network (CNN) features u...

Automatic Classification of Bright Retinal Lesions via Deep Network Features

The diabetic retinopathy is timely diagonalized through color eye fundus...

Learning CNN filters from user-drawn image markers for coconut-tree image classification

Identifying species of trees in aerial images is essential for land-use ...

A game method for improving the interpretability of convolution neural network

Real artificial intelligence always has been focused on by many machine ...

Low complexity convolutional neural network for vessel segmentation in portable retinal diagnostic devices

Retinal vessel information is helpful in retinal disease screening and d...

Uncertainty Propagation in Convolutional Neural Networks: Technical Report

In this technical report we study the problem of propagation of uncertai...

Convolutional Neural Network with Pruning Method for Handwritten Digit Recognition

CNN model is a popular method for imagery analysis, so it could be utili...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.