Research on Feature Extraction Method of Fundus Image Based on Deep Learning

2020 ◽  
Author(s):  
Xiaohui Cheng ◽  
Xianfeng Feng ◽  
Wei Li
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Fundus Image ◽  
Feature Extraction Method

scholarly journals Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations

2020 ◽  
Vol 10 (16) ◽  
pp. 5582
Author(s):  
Xiaochen Yuan ◽  
Tian Huang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Spatial Domain ◽  
Feature Extraction Method ◽  
Novel Approach ◽  
Image Postprocessing ◽  
Learning Technique ◽  
Residual Values

In this paper, a novel approach that uses a deep learning technique is proposed to detect and identify a variety of image operations. First, we propose the spatial domain-based nonlinear residual (SDNR) feature extraction method by constructing residual values from locally supported filters in the spatial domain. By applying minimum and maximum operators, diversity and nonlinearity are introduced; moreover, this construction brings nonsymmetry to the distribution of SDNR samples. Then, we propose applying a deep learning technique to the extracted SDNR features to detect and classify a variety of image operations. Many experiments have been conducted to verify the performance of the proposed approach, and the results indicate that the proposed method performs well in detecting and identifying the various common image postprocessing operations. Furthermore, comparisons between the proposed approach and the existing methods show the superiority of the proposed approach.

scholarly journals Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

2020 ◽  
Vol 11 ◽  
Author(s):  
Fatima Khan ◽  
Mukhtaj Khan ◽  
Nadeem Iqbal ◽  
Salman Khan ◽  
Dost Muhammad Khan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Learning Approach ◽  
Feature Extraction Method ◽  
Hybrid Feature Extraction

scholarly journals Research on Crop Leaf Disease Identification Method Based on LM-BP Neural Network

2019 ◽  
Vol 131 ◽  
pp. 01118
Author(s):  
Fan Tongke
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Bp Neural Network ◽  
Extraction Method ◽  
Large Scale ◽  
Disease Diagnosis ◽  
Learning Technology ◽  
Feature Extraction Method ◽  
Disease Identification

Aiming at the problem of disease diagnosis of large-scale crops, this paper combines machine vision and deep learning technology to propose an algorithm for constructing disease recognition by LM_BP neural network. The images of multiple crop leaves are collected, and the collected pictures are cut by image cutting technology, and the data are obtained by the color distance feature extraction method. The data are input into the disease recognition model, the feature weights are set, and the model is repeatedly trained to obtain accurate results. In this model, the research on corn disease shows that the model is simple and easy to implement, and the data are highly reliable.

scholarly journals The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

2016 ◽  
Vol 6 (6) ◽  
pp. 2797
Author(s):  
Anindita Septiarini ◽  
Hamdani Hamdani ◽  
Dyna Marisa Khairina
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Support Vector ◽  
Fundus Images ◽  
Contour Extraction ◽  
Fundus Image ◽  
Feature Extraction Method ◽  
Glaucoma Detection ◽  
Vector Machines ◽  
The World

<p>Glaucoma is the second leading cause of blindness in the world; therefore the detection of glaucoma is required. The detection of glaucoma is used to distinguish whether a patient's eye is normal or glaucoma. An expert observed the structure of the retina using fundus image to detect glaucoma. In this research, we propose feature extraction method based on cup area contour using fundus images to detect glaucoma. Our proposed method has been evaluated on 44 fundus images consisting of 23 normal and 21 glaucoma. The data is divided into two parts: firstly, used to the learning phase and secondly, used to the testing phase. In order to identify the fundus images including the class of normal or glaucoma, we applied Support Vector Machines (SVM) method. The performance of our method achieves the accuracy of 94.44%.</p>

Feature extraction method of analog circuit based on deep learning

2020 ◽  
Author(s):  
Jing Hu ◽  
Di Wu ◽  
Xiaopeng Zhou ◽  
Yulong Wang ◽  
Yan Xu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Analog Circuit ◽  
Feature Extraction Method

Dynamic Feature Extraction Method of Phone Speaker Based on Deep Learning

2020 ◽  
Vol 13 ◽  
Author(s):  
Hongbing Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Speech Recognition ◽  
Extraction Method ◽  
Good Effect ◽  
Dynamic Feature ◽  
Dynamic Features ◽  
Feature Extraction Method ◽  
Speech Feature ◽  
Speech Feature Extraction

: Nowadays, speech recognition has become one of the important technologies for human-computer interaction. Speech recognition is essentially a process of speech training and pattern recognition, which makes feature extraction technology particularly important. The quality of feature extraction is directly related to the accuracy of speech recognition. Dynamic feature parameters can effectively improve the accuracy of speech recognition, which makes the speech feature dynamic feature extraction has higher research value. The traditional dynamic feature extraction method is easy to generate more redundant information, resulting in low recognition accuracy. Therefore, based on a new speech feature extraction method, a method based on deep learning for speech feature extraction is proposed. Firstly, speech signal is preprocessed by pre-emphasis, windowing, filtering and endpoint detection. Then, the sliding differential cepstral feature (SDC) is extracted, which contains the voice information of the front and back frames. Finally, the feature is used as input to extract the dynamic features that represent the depth essence of speech information through the deep self-encoding neural network. The simulation results show that the dynamic features extracted by in-depth learning have better recognition performance than the original features, and have a good effect in speech recognition.

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