Organ-Based Medical Image Classification Using Support Vector Machine

2017 ◽  
Vol 8 (1) ◽  
pp. 18-30 ◽  
Author(s):  
Monali Y. Khachane
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Medical Image ◽  
Brain Mri ◽  
Texture Features ◽  
Classification Problem ◽  
Training Data ◽  
Support Vector ◽  
Medical Image Classification ◽  
Occurrence Matrix

Computer-Aided Detection/Diagnosis (CAD) through artificial Intelligence is emerging ara in Medical Image processing and health care to make the expert systems more and more intelligent. The aim of this paper is to analyze the performance of different feature extraction techniques for medical image classification problem. Efforts are made to classify Brain MRI and Knee MRI medical images. Gray Level Co-occurrence Matrix (GLCM) based texture features, DWT and DCT transform features and Invariant Moments are used to classify the data. Experimental results shown that the proposed system produced better results however the training data is less than testing data. Support Vector Machine classifier with linear kernel produced higher accuracy 100% when used with texture features.

scholarly journals A Hybrid Classification Method of Medical Image Based on Deep Learning

2021 ◽  
Author(s):  
Yulong Wang ◽  
Xiaofeng Liao ◽  
Dewen Qiao ◽  
Jiahui Wu
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Medical Image ◽  
Rapid Development ◽  
Medical Science ◽  
Image Features ◽  
Image Feature ◽  
Classification Method ◽  
Support Vector ◽  
Medical Image Classification

Abstract With the rapid development of modern medical science and technology, medical image classification has become a more and more challenging problem. However, in most traditional classification methods, image feature extraction is difficult, and the accuracy of classifier needs to be improved. Therefore, this paper proposes a high-accuracy medical image classification method based on deep learning, which is called hybrid CQ-SVM. Specifically, we combine the advantages of convolutional neural network (CNN) and support vector machine (SVM), and integrate the novel hybrid model. In our scheme, quantum-behaved particle swarm optimization algorithm (QPSO) is adopted to set its parameters automatically for solving the SVM parameter setting problem, CNN works as a trainable feature extractor and SVM optimized by QPSO performs as a trainable classifier. This method can automatically extract features from original medical images and generate predictions. The experimental results show that this method can extract better medical image features, and achieve higher classification accuracy.

Support Vector Machine Classifier with WHM Offset for Unbalanced Data

2008 ◽  
Vol 12 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Boyang Li ◽  
◽  
Jinglu Hu ◽  
Kotaro Hirasawa
Keyword(s):  
Support Vector Machine ◽  
Real World ◽  
Support Vector Machine Classifier ◽  
Classification Problem ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Real World Data ◽  
Unbalanced Classification ◽  
Improved Support Vector Machine

We propose an improved support vector machine (SVM) classifier by introducing a new offset, for solving the real-world unbalanced classification problem. The new offset is calculated based on the unbalanced support vectors resulting from the unbalanced training data. We developed a weighted harmonic mean (WHM) algorithm to further reduce the effects of noise on offset calculation. We apply the proposed approach to classify real-world data. Results of simulation demonstrate the effectiveness of our proposed approach.

scholarly journals Automatic Generation Of Training Data For Hyperspectral Image Classification Using Support Vector Machine

2015 ◽  
Vol XL-7/W3 ◽  
pp. 575-580 ◽  
Author(s):  
B. Abbasi ◽  
H. Arefi ◽  
B. Bigdeli ◽  
S. Roessner
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Satellite Image ◽  
Automatic Generation ◽  
Training Data ◽  
Experimental Result ◽  
Support Vector ◽  
Feature Descriptors

An image classification method based on Support Vector Machine (SVM) is proposed on hyperspectral and 3K DSM data. To obtain training data we applied an automatic method relating to four classes namely; building, grass, tree, and ground pixels. First, some initial segments regarding to building, tree, grass, and ground pixels are produced using different feature descriptors. The feature descriptors are generated using optical (hyperspectral) as well as range (3K DSM) images. The initial building regions are created using DSM segmentation. Fusion of NDVI and elevation information assist us to provide initial segments regarding to the grass and tree areas. Also, we created initial segment regarding to ground pixel after geodesic based filtering of DSM and elimination of the non-ground pixels. To improve classification accuracy, the hyperspectral image and 3K DSM were utilized simultaneously to perform image classification. For obtaining testing data, labelled pixels was divide into two parts: test and training. Experimental result shows a final classification accuracy of about 90% using Support Vector Machine. In the process of satellite image classification; provided by 3K camera. Both datasets correspond to Munich area in Germany.

scholarly journals SUPPORT VECTOR MACHINE AND DECISION TREE BASED CLASSIFICATION OF SIDE-SCAN SONAR MOSAICS USING TEXTURAL FEATURES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 27-34 ◽  
Author(s):  
H. K. Febriawan ◽  
P. Helmholz ◽  
I. M. Parnum
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Image Classification ◽  
Texture Features ◽  
Supervised Machine Learning ◽  
Sidescan Sonar ◽  
Support Vector ◽  
Side Scan Sonar ◽  
Gaussian Kernels

<p><strong>Abstract.</strong> The diversity and heterogeneity of coastal, estuarine and stream habitats has led to them becoming a prevalent topic for study. Woody ruins are areas of potential riverbed habitat, particularly for fish. Therefore, the mapping of those areas is of interest. However, due to the limited visibility in some river systems, satellites, airborne or other camera-based systems (passive systems) cannot be used. By contrast, sidescan sonar is a popular underwater acoustic imaging system that is capable of providing high- resolution monochromatic images of the seafloor and riverbeds. Although the study of sidescan sonar imaging using supervised classification has become a prominent research subject, the use of composite texture features in machine learning classification is still limited. This study describes an investigation of the use of texture analysis and feature extraction on side-scan sonar imagery in two supervised machine learning classifications: Support Vector Machine (SVM) and Decision Tree (DT). A combination of first- order texture and second-order texture is investigated to obtain the most appropriate texture features for the image classification. SVM, using linear and Gaussian kernels along with Decision Tree classifiers, was examined using selected texture features. The results of overall accuracy and kappa coefficient revealed that SVM using a linear kernel leads to a more promising result, with 77% overall accuracy and 0.62 kappa, than SVM using either a Gaussian kernel or Decision Tree (60% and 73% overall accuracy, and 0.39 and 0.59 kappa, respectively). However, this study has demonstrated that SVM using linear and Gaussian kernels as well as a Decision Tree makes it capable of being used in side-scan sonar image classification and riverbed habitat mapping.</p>

Data Augmentation in Logit Space for Medical Image Classification with Limited Training Data

2021 ◽  
pp. 469-479
Author(s):  
Yangwen Hu ◽  
Zhehao Zhong ◽  
Ruixuan Wang ◽  
Hongmei Liu ◽  
Zhijun Tan ◽  
...  
Keyword(s):  
Image Classification ◽  
Medical Image ◽  
Data Augmentation ◽  
Training Data ◽  
Medical Image Classification

scholarly journals PERCEIVING DIGITAL WATERMARK DETECTION AS IMAGE CLASSIFICATION PROBLEM

2016 ◽  
Vol 2 (1) ◽  
pp. 1-22
Author(s):  
P. Then ◽  
Y.C. Wang
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
False Negative ◽  
Digital Watermark ◽  
Classification Problem ◽  
Support Vector ◽  
Svm Classifier ◽  
Operating Characteristics ◽  
Negative Class ◽  
Correlation Detector

Digital watermark detection is treated as classification problem of image processing. For image classification that searches for a butterfly, an image can be classified as positive class that is a butterfly and negative class that is not a butterfly. Similarly, the watermarked and unwatermarked images are perceived as positive and negative class respectively. Hence, Support Vector Machine (SVM) is used as the classifier of watermarked and unwatermarked digital image due to its ability of separating both linearly and non-linearly separable data. Hyperplanes of various detectors are briefly elaborated to show how SVM's hyperplane is suitable for Stirmark attacked watermarked image. Cox’s spread spectrum watermarking scheme is used to embed the watermark into digital images. Then, Support Vector Machine is trained with both the watermarked and unwatermarked images. Training SVM eliminates the use of watermark during the detection process. Receiver Operating Characteristics (ROC) graphs are plotted to assess the false positive and false negative probability of both the correlation detector of the watermarking schemes and SVM classifier. Both watermarked and unwatermarked images are later attacked under Stirmark, and then tested on the correlation detector and SVM classifier. Remedies are suggested to preprocess the training data. The optimal setting of SVM parameters is also investigated and determined besides preprocessing. The preprocessing and optimal parameters setting enable the trained SVM to achieve substantially better results than those resulting from the correlation detector.

Static Signature Verification Based on Texture Analysis Using Support Vector Machine

2017 ◽  
Vol 8 (2) ◽  
pp. 22-32 ◽  
Author(s):  
Subhash Chandra ◽  
Sushila Maheshkar
Keyword(s):  
Support Vector Machine ◽  
Texture Features ◽  
Signature Verification ◽  
Support Vector ◽  
Gray Level ◽  
Global Level ◽  
First Order ◽  
Svm Model ◽  
Level Information ◽  
Occurrence Matrix

Off-line hand written signature verification performs at the global level of image. It processes the gray level information in the image using statistical texture features. The textures and co-occurrence matrix are analyzed for features extraction. A first order histogram is also processed to reduce different writing ink pens used by signers. Samples of signature are trained with SVM model where random and skilled forgeries have been used for testing. Experimental results are performed on two databases: MCYT-75 and GPDS Synthetic Signature Corpus.

ML2S-SVM: multi-label least-squares support vector machine classifiers

2019 ◽  
Vol 37 (6) ◽  
pp. 1040-1058 ◽  
Author(s):  
Shuo Xu ◽  
Xin An
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Image Classification ◽  
Classification Problem ◽  
Superior Performance ◽  
Support Vector ◽  
Data Sets ◽  
Classification Problems ◽  
Content Type ◽  
Classification Approach

Purpose Image classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance. Design/methodology/approach Many multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM (ML2S-SVM). Findings Experimental results on the emotions, scene, yeast and bibtex data sets indicate that the ML2S-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of ML2S-SVM, so it is necessary for users to identify proper parameters in advance. Originality/value On the basis of MTLS-SVM, a novel multi-label classification approach, ML2S-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification.

Sign in / Sign up

Export Citation Format

Share Document