Deep learning neural network for texture feature extraction in oral cancer: enhanced loss function

2020 ◽  
Vol 79 (37-38) ◽  
pp. 27867-27890 ◽  
Author(s):  
Bishal Bhandari ◽  
Abeer Alsadoon ◽  
P. W. C. Prasad ◽  
Salma Abdullah ◽  
Sami Haddad
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Oral Cancer ◽  
Loss Function ◽  
Texture Feature ◽  
Texture Feature Extraction ◽  
Deep Learning Neural Network

scholarly journals Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

2020 ◽  
Vol 16 (3) ◽  
pp. 280-294 ◽  
Author(s):  
Toto Haryanto ◽  
Adib Pratama ◽  
Heru Suhartanto ◽  
Aniati Murni ◽  
Kusmardi Kusmardi ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Neural Network ◽  
Texture Feature ◽  
Gpu Acceleration ◽  
Texture Feature Extraction ◽  
Histopathology Images

scholarly journals IDENTIFICATION OF NITROGEN STATUS IN Brassica juncea L.USING COLOR MOMENT GLCM AND BACKPROPAGATION NEURAL NETWORK

2020 ◽  
Author(s):  
Satya Kumara
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Feature Extraction ◽  
Brassica Juncea ◽  
Back Propagation ◽  
Texture Feature ◽  
Back Propagation Neural Network ◽  
Texture Feature Extraction ◽  
Nitrogen Status ◽  
Color Moment

Vegetables cultivation using hydroponic is becoming popular now days because of its irrigation and fertilizer efficiency. One type of vegetable which can be cultivated using hydroponic is green mustard (Brassica juncea L.) tosakan variety. This vegetable is harvested in the vegetative phase, approximately aged of 30 days after planting. In addition, during the vegetative phase, this plant requires more nitrogen for growth of vegetative organs. The lack of nitrogen will lead to slow growth and the leaves turn yellow. In this study, non-destructive technology was developed to identify nitrogen status through the image of green mustard leaf by using digital image processing and artificial neural network. The image processing method used was the color moment for color feature extraction, gray level co-occurrence matrix (GLCM) for texture feature extraction and back propagation neural network to identify nitrogen status from the image of leaf. The input image data resulted from acquisition process was RGB color image which was converted to HSV. Prior to the color and texture feature extraction and texture, acquisition image was segmented and cropped to get the leaf image only. Next Step was to conduct training using back propagation neural network with two hidden layer combinations, 20,000 iteration epoch. Accuracy of the test results using those methods was 97.82%. The result indicates those three methods is reliable to identify nitrogen status in the leaf of green mustard.

Automatic system for grading banana using GLCM texture feature extraction and neural network arbitrations

2017 ◽  
Vol 40 (6) ◽  
pp. e12575 ◽  
Author(s):  
Ebenezer O. Olaniyi ◽  
Adefemi A. Adekunle ◽  
Temitope Odekuoye ◽  
Adnan Khashman
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Automatic System ◽  
Texture Feature ◽  
Texture Feature Extraction

scholarly journals SURVEY ON EVOLVING DEEP LEARNING NEURAL NETWORK ARCHITECTURES

2019 ◽  
Vol 2019 (2) ◽  
pp. 73-82 ◽  
Author(s):  
Dr. Abul Bashar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Deep Learning ◽  
Human Performance ◽  
Deep Neural Networks ◽  
Network Architectures ◽  
Neural Network Architectures ◽  
Deep Learning Neural Network

The deep learning being a subcategory of the machine learning follows the human instincts of learning by example to produce accurate results. The deep learning performs training to the computer frame work to directly classify the tasks from the documents available either in the form of the text, image, or the sound. Most often the deep learning utilizes the neural network to perform the accurate classification and is referred as the deep neural networks; one of the most common deep neural networks used in a broader range of applications is the convolution neural network that provides an automated way of feature extraction by learning the features directly from the images or the text unlike the machine learning that extracts the features manually. This enables the deep learning neural networks to have a state of art accuracy that mostly expels even the human performance. So the paper is to present the survey on the deep learning neural network architectures utilized in various applications for having an accurate classification with an automated feature extraction.

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