scholarly journals Research on Ultrasonic Image Recognition Based on Optimization Immune Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xueqiang Zeng ◽  
Sufen Chen
Keyword(s):  
Support Vector Machine ◽  
Liver Cirrhosis ◽  
Liver Cancer ◽  
Image Classification ◽  
Image Recognition ◽  
Normal Liver ◽  
Immune Algorithm ◽  
Support Vector ◽  
Ultrasound Images ◽  
Liver Image

With the rapid development of science and technology, ultrasound has been paid more and more attention by people, and it is widely used in engineering, diagnosis, and detection. In this paper, an ultrasonic image recognition method based on immune algorithm is proposed for ultrasonic images, and its method is applied to medical ultrasound liver image recognition. Firstly, this paper grays out the ultrasound liver image and selects the region of interest of the image. Secondly, it extracts the feature based on spatial gray matrix independent matrix, spatial frequency decomposition, and fractal features. Then, the immune algorithm is used to classify and identify the normal liver, liver cirrhosis, and liver cancer ultrasound images. Finally, based on the deficiency of the immune algorithm, it is combined with the support vector machine to form an optimized immune algorithm, which improves the performance of ultrasonic liver image classification and recognition. The simulation shows that this paper can effectively classify the normal liver, liver cirrhosis, and liver cancer ultrasound images. Compared with the traditional immune algorithm, this paper combines the immune algorithm with the support vector machine, and the optimized immune algorithm can effectively improve the performance of ultrasonic liver image classification and recognition.

Application of Fuzzy Enhancement in the Diagnosis of Liver Cancer from Ultrasound Images

2012 ◽  
Vol 195-196 ◽  
pp. 493-497
Author(s):  
Wu Qiu ◽  
Feng Xiao ◽  
Xin Yang ◽  
Mao Lin Ye ◽  
Yu Chi Ming ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Back Propagation ◽  
Normal Liver ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Ultrasound Images ◽  
Gray Level ◽  
Fuzzy Enhancement ◽  
Computer Aided ◽  
Aided Diagnosis

Fuzzy enhancement is applied in computer aided diagnosis of liver cancer from B mode ultrasound images as a pre-processing procedure in this paper. It was evaluated with three classifiers including K means, back propagation neural network and support vector machine using 25 features from single gray-level statistic, gray-level co-occurrence matrix (GLCM), and gray-level run-length matrix (GLRLM). The results show that the fuzzy enhancement algorithm can improve classification accuracy of normal liver, liver cancer and Hemangioma from B mode ultrasound images for three classifiers. It is proved that fuzzy enhancement as an efficient preprocessing procedure could be used in the computer aided diagnosis system of liver cancer.

A Novel Technique for Subpixel Image Classification Based on Support Vector Machine

2010 ◽  
Vol 19 (11) ◽  
pp. 2983-2999 ◽  
Author(s):  
Francesca Bovolo ◽  
Lorenzo Bruzzone ◽  
Lorenzo Carlin
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Support Vector ◽  
Novel Technique

SAR Image Classification by Support Vector Machine

2007 ◽  
pp. 341-353
Author(s):  
Toru Fujinaka ◽  
Michifumi Yoshioka ◽  
Sigeru Omatu
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Support Vector ◽  
Sar Image

scholarly journals A One-shot Learning Approach to Image Classification using Genetic Programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
M Johnston
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Genetic Programming ◽  
Image Classification ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Learning Approach ◽  
Data Sets ◽  
Domain Specific ◽  
International Publishing

In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naïve Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features. © Springer International Publishing 2013.

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