scholarly journals Transfer Learning based Multi-label Classification of Images

2019 ◽  
Vol 7 (3) ◽  
pp. 2642-2656
Author(s):  
K. S. Anand
Keyword(s):  
Transfer Learning ◽  
Classification Of Images

Técnicas De Inteligencia Artificial Aplicadas A Pruebas Capilaroscopicas Para La Detección De Enfermedades Autoinmunes Que Comprometen La Circulación Sanguínea

2021 ◽  
Author(s):  
◽  
M. Alvarado
Keyword(s):  
Artificial Intelligence ◽  
Transfer Learning ◽  
Automatic Classification ◽  
Diseased Subject ◽  
Classification Of Images

The main purpose of this paper is the development of an artificial intelligence model for the automatic classification of images, in order to optimize the detection of pathologies through capillaroscopy tests of the nail fold, this technique allows obtaining images of the morphology of the capillaries in the proximal nail fold of the hands. We used a database that consists of 300 images of capillaries corresponding to the nail fold. These images were labeled as healthy or diseased subject depending on the patterns of the capillaries. The method used to classify the images into two classes was transfer learning from a MobileNet V2 base model. The results show that the network is capable of detecting the presence of pathological patterns in the capillaries with a precision of 96.667%.

scholarly journals Dog Breed Identification with Fine tuning of Pre-trained models

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3677-3680
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Classification Problem ◽  
Training Data ◽  
Fine Tuning ◽  
Dog Breed ◽  
Classification Of Images ◽  
Multiclass Classifier

Dog Breed identification is a specific application of Convolutional Neural Networks. Though the classification of Images by Convolutional Neural Network serves to be efficient method, still it has few drawbacks. Convolutional Neural Networks requires a large amount of images as training data and basic time for training the data and to achieve higher accuracy on the classification. To overcome this substantial time we use Transfer Learning. In computer vision, transfer learning refers to the use of a pre-trained models to train the CNN. By Transfer learning, a pre-trained model is trained to provide solution to classification problem which is similar to the classification problem we have. In this project we are using various pre-trained models like VGG16, Xception, InceptionV3 to train over 1400 images covering 120 breeds out of which 16 breeds of dogs were used as classes for training and obtain bottleneck features from these pre-trained models. Finally, Logistic Regression a multiclass classifier is used to identify the breed of the dog from the images and obtained 91%, 94%,95% validation accuracy for these different pre-trained models VGG16, Xception, InceptionV3.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Using clusterization principles for the selection of repair sections of main oil pipelines based on diagnostic data

2011 ◽  
Vol 8 (1) ◽  
pp. 201-210
Author(s):  
R.M. Bogdanov
Keyword(s):  
Cluster Analysis ◽  
Defect Density ◽  
Density Variation ◽  
Distance Functions ◽  
Oil Pipeline ◽  
Oil Pipelines ◽  
Classification Of Images ◽  
Main Oil Pipeline ◽  
Selection Of

The problem of determining the repair sections of the main oil pipeline is solved, basing on the classification of images using distance functions and the clustering principle, The criteria characterizing the cluster are determined by certain given values, based on a comparison with which the defect is assigned to a given cluster, procedures for the redistribution of defects in cluster zones are provided, and the cluster zones parameters are being changed. Calculations are demonstrating the range of defect density variation depending on pipeline sections and the universal capabilities of linear objects configuration with arbitrary density, provided by cluster analysis.

scholarly journals Transfer learning networks with skip connections for classification of brain tumors

2021 ◽  
Author(s):  
Saleh Alaraimi ◽  
Kenneth E. Okedu ◽  
Hugo Tianfield ◽  
Richard Holden ◽  
Omair Uthmani
Keyword(s):  
Brain Tumors ◽  
Transfer Learning ◽  
Learning Networks

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

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