A Deep Neural Network Based on ELM for Semi-supervised Learning of Image Classification

2017 ◽  
Vol 48 (1) ◽  
pp. 375-388 ◽  
Author(s):  
Peiju Chang ◽  
Jiangshe Zhang ◽  
Junying Hu ◽  
Zengjie Song
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Supervised Learning ◽  
Deep Neural Network

scholarly journals Image Classification Using Transfer Learning and Deep Learning

2021 ◽  
Vol 10 (9) ◽  
pp. 25394-25398
Author(s):  
Chitra Desai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Deep Neural Network ◽  
Visual Recognition ◽  
Classification Of Images

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used.   This paper demonstrates image classification using pretrained deep neural network model VGG16 which is trained on images from ImageNet dataset. After obtaining the convolutional base model, a new deep neural network model is built on top of it for image classification based on fully connected network. This classifier will use features extracted from the convolutional base model.

scholarly journals Combining local and global hypotheses in deep neural network for multi-label image classification

2017 ◽  
Vol 235 ◽  
pp. 38-45 ◽  
Author(s):  
Qinghua Yu ◽  
Jinjun Wang ◽  
Shizhou Zhang ◽  
Yihong Gong ◽  
Jizhong Zhao
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Deep Neural Network

Semi-Supervised Learning and Deep Neural Network on Detection of Roadway Cracking Using Unmanned Aerial System Imagery

2021 ◽  
Author(s):  
Long Ngo Hoang Truong ◽  
Edward Clay ◽  
Omar E. Mora ◽  
Wen Cheng ◽  
Maninder Kaur ◽  
...  
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Deep Neural Network ◽  
Unmanned Aerial System

scholarly journals Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Abdullah-Al Nahid ◽  
Mohamad Ali Mehrabi ◽  
Yinan Kong
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Classification ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Statistical Information ◽  
Support Vector ◽  
Biomedical Image ◽  
Histopathological Images ◽  
Photography Analysis

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F-Measure value is achieved on both the 40x and 100x datasets.

scholarly journals Image Classification: A Survey

2020 ◽  
Vol 1 (2) ◽  
pp. 1-9
Author(s):  
Ankita Singh ◽  
◽  
Pawan Singh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Learning Ability ◽  
Final Decision

The Classification of images is a paramount topic in artificial vision systems which have drawn a notable amount of interest over the past years. This field aims to classify an image, which is an input, based on its visual content. Currently, most people relied on hand-crafted features to describe an image in a particular way. Then, using classifiers that are learnable, such as random forest, and decision tree was applied to the extract features to come to a final decision. The problem arises when large numbers of photos are concerned. It becomes a too difficult problem to find features from them. This is one of the reasons that the deep neural network model has been introduced. Owing to the existence of Deep learning, it can become feasible to represent the hierarchical nature of features using a various number of layers and corresponding weight with them. The existing image classification methods have been gradually applied in real-world problems, but then there are various problems in its application processes, such as unsatisfactory effect and extremely low classification accuracy or then and weak adaptive ability. Models using deep learning concepts have robust learning ability, which combines the feature extraction and the process of classification into a whole which then completes an image classification task, which can improve the image classification accuracy effectively. Convolutional Neural Networks are a powerful deep neural network technique. These networks preserve the spatial structure of a problem and were built for object recognition tasks such as classifying an image into respective classes. Neural networks are much known because people are getting a state-of-the-art outcome on complex computer vision and natural language processing tasks. Convolutional neural networks have been extensively used.

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