scholarly journals Automatic Classification Algorithm for Diffused Liver Diseases based on Ultrasound Images

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ahmed Gaber ◽  
A. Hamdy ◽  
Hammam M. Abdelaal ◽  
Ahmed Elkattan ◽  
Motasem M. Elshourbagy ◽  
...  
Keyword(s):  
Liver Diseases ◽  
Automatic Classification ◽  
Classification Algorithm ◽  
Ultrasound Images

scholarly journals OP01.09: Automatic classification of fetal ultrasound images: a remarkable application of deep learning technology for fetal medicine

2019 ◽  
Vol 54 (S1) ◽  
pp. 86-87
Author(s):  
X.P. Burgos‐Artizuu ◽  
E. Eixarch ◽  
D. Coronado‐Gutierrez ◽  
B. Valenzuela ◽  
E. Bonet‐Carne ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Learning Technology ◽  
Ultrasound Images ◽  
Fetal Ultrasound ◽  
Fetal Medicine

scholarly journals New insights into ambient and focal visual fixations using an automatic classification algorithm

i-Perception ◽  
10.1068/i0414 ◽  
2011 ◽  
Vol 2 (6) ◽  
pp. 592-610 ◽  
Author(s):  
Brice Follet ◽  
Olivier Le Meur ◽  
Thierry Baccino
Keyword(s):  
Automatic Classification ◽  
Classification Algorithm ◽  
Visual Fixations

scholarly journals Classification for Liver Diseases Based on Ultrasound Image Texture Features

2018 ◽  
Author(s):  
Sendren Sheng-Dong Xu ◽  
Chien-Tien Su ◽  
Chun-Chao Chang ◽  
Pham Quoc Phu
Keyword(s):  
Liver Abscess ◽  
Liver Diseases ◽  
Ultrasound Image ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Gray Level ◽  
Forward Selection

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

scholarly journals Automatic Classification Algorithm for Multisearch Data Association Rules in Wireless Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Cailing Li ◽  
Wenjun Li
Keyword(s):  
Wireless Network ◽  
Association Rules ◽  
Data Association ◽  
Automatic Classification ◽  
Classification Algorithm ◽  
Fuzzy Classification ◽  
Classification Error ◽  
Data Generation ◽  
High Coverage ◽  
Efficient Data

In order to realize efficient data processing in wireless network, this paper designs an automatic classification algorithm of multisearch data association rules in a wireless network. According to the algorithm, starting from the mining of multisearch data association rules, from the discretization of continuous attributes of multisearch data, generation of fuzzy classification rules, and the design of association rule classifier and other aspects, automatic classification is completed by using the mining results. Experimental results show that this algorithm has the advantages of small classification error, good real-time performance, high coverage rate, and high feasibility.

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