CERVICAL CANCER DETECTION AND CLASSIFICATION BY USING EFFECTUAL INTEGRATION OF DIRECTIONAL GABOR TEXTURE FEATURE EXTRACTION AND HYBRID KERNEL BASED SUPPORT VECTOR CLASSIFICATION

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

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Breast Cancer Detection Using Combination of Feature Extraction Models

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.12.16549 ◽

2018 ◽

Vol 7 (3.12) ◽

pp. 848

Author(s):

T Suneetha Rani ◽

S J Soujanya ◽

Pole Anjaiah

Keyword(s):

Breast Cancer ◽

Feature Extraction ◽

Cancer Detection ◽

Texture Feature ◽

Region Of Interest ◽

Computation Time ◽

Morphological Operations ◽

Texture Feature Extraction ◽

Mammogram Images

Recognition of either masses or tissues in a mammogram digital images is a key issue for radiologist. Present methods uses medial filter and morphological operations for detection of suspected cases in a mammogram. They use region of interest (ROI) segmentation for extraction of masses and classification of levels of severities. Classification of large number of mammogram images based on breast cancer cases takes longer computation time for performing of ROI segmentation. This is addressed by multi-ROI segmentation and it retrieves the textual properties of large mammogram images for effectively determining the breast cancer mammogram images.Experimental results shows the better performance of proposed method than existing ROI based texture feature extraction.

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Comparative Analysis of Texture Patterns on Mammograms for Classification

Traitement du signal ◽

10.18280/ts.380215 ◽

2021 ◽

Vol 38 (2) ◽

pp. 379-386

Author(s):

Nagadevi Darapureddy ◽

Nagaprakash Karatapu ◽

Tirumala Krishna Battula

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Discriminant Analysis ◽

Classification Accuracy ◽

Texture Feature ◽

Gradient Boosting ◽

Support Vector ◽

Texture Feature Extraction ◽

Support Vector Classifier ◽

Tree Classifier

Breast cancer is a cancerous tumor that arrives within the tissues of the breast. Women are mostly attacked than men. To detect early cancer medical specialists, suggest mammography for screening. Algorithms in Machine learning were executed on mammogram images to classify whether the tissues are deleterious or not. An analysis is done based on the texture feature extraction using different techniques like Frequency decoded local binary pattern (FDLBP), Local Bit-plane Decoded Pattern (LBDP), Local Diagonal Extrema Pattern (LDEP), Local Directional Order Pattern (LDOP), Local Wavelet Pattern (LWP). The features extracted are tested on 322 images from MIA’s database of three different classes. The algorithms in Machine learning like K-Nearest Neighbor classifier (KNN), Support vector classifier (SVC), Decision Tree classifier (DTC), Random Forest classifier (RFC), AdaBoost classifier (AC), Gradient Boosting classifier (GBC), Gaussian Naive Bayes classifier (GNB), Linear Discriminant Analysis classifier (LDA), Quadratic Discriminant Analysis classifier (QDA) were used to evaluate the accuracy of classification. This paper examines the comparison of accuracy using different texture features. KNN algorithm with LDEP for texture feature extraction gives classification accuracy of 64.61%, SVC with all the texture patterns mentioned gives classification accuracy of 63.07%, DTC with FDLBP, LBDP gives classification accuracy of 47.69, RFC with LBDP and AC with LDOP and GBC with FDLBP gives 61.53% classification accuracy, GNB and LDA with FDLBP gives 60% and 63.07% classification accuracy respectively, QDA with LBDP gives 64.61 classification accuracy. Of all the Algorithms support vector classifier gives good accuracy results with all the texture patterns mentioned.

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Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-179970 ◽

2020 ◽

Vol 39 (4) ◽

pp. 4847-4858

Author(s):

Lei Wang ◽

Jinhai Sun ◽

Tuojian Li

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Extraction Method ◽

Feature Recognition ◽

Texture Feature ◽

Texture Features ◽

Spatial Domain ◽

Image Texture ◽

Support Vector ◽

Texture Feature Extraction

Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects.

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Texture feature extraction and classification for CT image of xinjiang local liver hydatid based on support vector machine

Information Technology ◽

10.1201/b18776-73 ◽

2015 ◽

pp. 395-400

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Texture Feature ◽

Support Vector ◽

Ct Image ◽

Texture Feature Extraction

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3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

Applied Sciences ◽

10.3390/app11052332 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2332

Author(s):

Stefania Barburiceanu ◽

Romulus Terebes ◽

Serban Meza

Keyword(s):

Feature Extraction ◽

Imaging Techniques ◽

Texture Feature ◽

Local Binary Patterns ◽

Image Feature ◽

Support Vector ◽

Volumetric Data ◽

Texture Feature Extraction ◽

Discrimination Power ◽

Local Anisotropy

Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

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