Iris nevus diagnosis: convolutional neural network and deep belief network

Abstract

This work presents the diagnosis of iris nevus using a convolutional neural network (CNN) and deep belief network (DBN). Iris nevus is a pigmented growth (tumor) found in the front of the eye or around the pupil. It is seen that racial and environmental factors affect the iris color (e.g., blue, hazel, brown) of patients; hence, pigmented growths may be masked in the eye background or iris. In this work, some image processing techniques are applied to images to reinforce areas of interests in them, after which the considered classifiers are trained. We describe the automated diagnosis of iris nevus using neural network-based systems for the classification of eye images as "nevus affected" and "unaffected". Recognition rates of 93.35% and 93.67% were achieved for the CNN and DBN, respectively. Hence, the systems described in this work can be used satisfactorily for diagnosis or to reinforce the confidence in manual-visual diagnosis by medical experts.

Transfer Learning with Convolutional Neural Networks for IRIS Recognition

“…Convolutional Neural Network (CNN) consists of three types of layers. The layer can be either Convolutional, Pooling or fully connected [19]. For more details, the description of the layers is presented in Table 1. CNN is usually structured in two parts.…”

“…This layer used to decrease the size of the convolutional layer output while saving the most significant information contained in the input layer. The pooling operation can be (max or average), selecting of maximal value is the most common type of pooling operation [19].…”

“…The pertained convolutional neural network (LeNet-5) [19]. In this work, the authors proposed a system for the diagnosis of iris nevus depending on a CNN in addition to deep belief network.…”

“…Support Vector Machine which used in [6,19,45] this algorithm is a common machine learning algorithm that used for classification. SVM classifier makes classification through discovery the hyperplane that maximizes the margin among the two classes.…”

Convolutional Neural Network Based Feature Extraction for IRIS Recognition

“…A Convolutional Neural Network (CNN) comprises of alternating layers of locally connected convolutional layers where every layer has the same number of filters. downsampling layers, and the fully connected layers that work as a classifier [14]. Figure 1, shows the overall architecture of a CNN.…”