Transfer learning with multiple pre-trained network for fundus classification

Transfer learning (TL) is a technique of reuse and modify a pre-trained network. It reuses feature extraction layer at a pre-trained network. A target domain in TL obtains the features knowledge from the source domain. TL modified classification layer at a pre-trained network. The target domain can do new tasks according to a purpose. In this article, the target domain is fundus image classification includes normal and neovascularization. Data consist of 100 patches. The comparison of training and validation data was 70:30. The selection of training and validation data is done randomly. Steps of TL i.e load pre-trained networks, replace final layers, train the network, and assess network accuracy. First, the pre-trained network is a layer configuration of the convolutional neural network architecture. Pre-trained network used are AlexNet, VGG16, VGG19, ResNet50, ResNet101, GoogLeNet, Inception-V3, InceptionResNetV2, and squeezenet. Second, replace the final layer is to replace the last three layers. They are fully connected layer, softmax, and output layer. The layer is replaced with a fully connected layer that classifies according to number of classes. Furthermore, it's followed by a softmax and output layer that matches with the target domain. Third, we trained the network. Networks were trained to produce optimal accuracy. In this section, we use gradient descent algorithm optimization. Fourth, assess network accuracy. The experiment results show a testing accuracy between 80% and 100%

Classification of neovascularization using convolutional neural network model

Neovascularization is a new vessel in the retina beside the artery-venous. Neovascularization can appear on the optic disk and the entire surface of the retina. The retina categorized in Proliferative Diabetic Retinopathy (PDR) if it has neovascularization. PDR is a severe Diabetic Retinopathy (DR). An image classification system between normal and neovascularization is here presented. The classification using Convolutional Neural Network (CNN) model and classification method such as Support Vector Machine, k-Nearest Neighbor, Naïve Bayes classifier, Discriminant Analysis, and Decision Tree. By far, there are no data patches of neovascularization for the process of classification. Data consist of normal, New Vessel on the Disc (NVD) and New Vessel Elsewhere (NVE). Images are taken from 2 databases, MESSIDOR and Retina Image Bank. The patches are made from a manual crop on the image that has been marked by experts as neovascularization. The dataset consists of 100 data patches. The test results using three scenarios obtained a classification accuracy of 90%-100% with linear loss cross validation 0%-26.67%. The test performs using a single Graphical Processing Unit (GPU)

Boundedness of the Riesz potential in generalized Morrey spaces

The purpose of this paper is to prove the necessary and sufficient condition for the boundedness of Riesz operators on homogeneous generalized Morrey spaces. Further, we will make use the Q-Ahlfors regularity condition in the proof instead of usual doubling conditions

KONSTRUKSI DIGRAF EKSENTRIS DARI DIGRAF TERBOBOTI

Dalam penelitian terdahulu telah dibangun digraf eksentris dari digraf dan digraf eksentris dari graf. Pada penelitian ini akan dibangun digraf eksentris dari digraf terboboti. Permasalahan penelitian ini adalah (1) bagaimana konstruksi jarak dua titik pada digraf terboboti #915;, (2) bagaimana konstruksi titik eksentris pada digraf terboboti #915;, dan (3) bagaimana konstruksi digraf eksentris dari digraf terboboti #915;. Tujuan penelitian ini adalah membangun konstruksi digraf eksentris dari digraf terboboti, melalui konstruksi jarak dua titik pada digraf terboboti dan konstruksi titik eksentris pada digraf terboboti. Dengan mengkaji hasil penelitian terdahulu dan dengan menggunakan metode konstruktif diperoleh hal-hal sebagai berikut: 1. Definisi Jarak Dua Titik pada Digraf Terboboti Jumlah bobot terkecil dari garis pada path dari u ke v disebut jarak dari titik u ke titik v, dinotasikan dengan d(u, v). 2. Definisi Titik Eksentris pada Digraf Terboboti Eksentrisitas dari titik u E #915;, dinotasikan dengan e(u), adalah maksimum jarak dari u ke sebarang titik dalam #915;. Titik v #1108; #915; disebut titik eksentris dari titik u, jika jarak u ke v sama dengan e(u). 3. Definisi Digraf Eksentris dari Digraf Terboboti Digraf eksentris dari digraf #915;, dinotasikan dengan (ED(#915;), adalah digraf dengan V(ED(#915;)) = V(#915;) dan E(ED(#915;)) = {(u,v) : u, v #1108; #915; dan v titik eksentris dari u}. Bobot (u,v) #1108; E(ED(#915;)) sama dengan eksentrisitas titik u. Dalam penelitian ini dibuat algoritma dan program MATLAB 6.5 untuk mencari di graf eksentris dari digraf #915;