scholarly journals An optimized SVM kernel for texture classification and its application in microcalcification detection

2021 ◽  
Author(s):  
Mahdi Sabri
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Medical Images ◽  
Texture Classification ◽  
Dimensional Space ◽  
Good Choice ◽  
Support Vector ◽  
Classification Problems ◽  
Diagnose Breast Cancer ◽  
Cad Systems

Mammograms, commonly used to diagnose breast cancer, are difficult medical images to interpret. Computer aided diagnosis (CAD) systems have the potential to assist radiologists by locating suspicious regions in the mammograms for more detailed examination. One approach is for CAD systems to detect microcalcification. This approach uses classification of texture features and has applications for the detection of breast cancer as well as other abnormalties in medical images. The Support Vector Machine (SVM) has been shown to be effective in texture classification. SVM performs well in high dimensional space such as the space spanned by texture images. The kernel function in SVM algorithm implicitly performs feature extraction. Since SVM is basically suited for two-class classification problems, it is potentially a good choice for several different medical imaging which deal with abnormality detection. The main contribution of this thesis in the sense of texture classification is proposing a new texture classification algorithm by effectively employing external features within SVM kernel and introducing a new feature extraction method for texture classification.

scholarly journals An optimized SVM kernel for texture classification and its application in microcalcification detection

2021 ◽  
Author(s):  
Mahdi Sabri
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Medical Images ◽  
Texture Classification ◽  
Dimensional Space ◽  
Good Choice ◽  
Support Vector ◽  
Classification Problems ◽  
Diagnose Breast Cancer ◽  
Cad Systems

Mammograms, commonly used to diagnose breast cancer, are difficult medical images to interpret. Computer aided diagnosis (CAD) systems have the potential to assist radiologists by locating suspicious regions in the mammograms for more detailed examination. One approach is for CAD systems to detect microcalcification. This approach uses classification of texture features and has applications for the detection of breast cancer as well as other abnormalties in medical images. The Support Vector Machine (SVM) has been shown to be effective in texture classification. SVM performs well in high dimensional space such as the space spanned by texture images. The kernel function in SVM algorithm implicitly performs feature extraction. Since SVM is basically suited for two-class classification problems, it is potentially a good choice for several different medical imaging which deal with abnormality detection. The main contribution of this thesis in the sense of texture classification is proposing a new texture classification algorithm by effectively employing external features within SVM kernel and introducing a new feature extraction method for texture classification.

scholarly journals Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1870
Author(s):  
Yaghoub Pourasad ◽  
Esmaeil Zarouri ◽  
Mohammad Salemizadeh Parizi ◽  
Amin Salih Mohammed
Keyword(s):  
Breast Cancer ◽  
High Performance ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Ultrasound Images ◽  
Morphological Operations ◽  
K Nearest Neighbor ◽  
Diagnose Breast Cancer ◽  
Premature Deaths ◽  
Infected Area

Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor’s location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.

Machine Learning Applications in Breast Cancer Diagnosis

2017 ◽  
pp. 465-490
Author(s):  
Syed Jamal Safdar Gardezi ◽  
Mohamed Meselhy Eltoukhy ◽  
Ibrahima Faye
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Extraction ◽  
Machine Learning Algorithms ◽  
Learning Tools ◽  
Extraction Techniques ◽  
Cad Systems ◽  
Cad System ◽  
Machine Learning Applications ◽  
Detection And Diagnosis

Breast cancer is one of the leading causes of death in women worldwide. Early detection is the key to reduce the mortality rates. Mammography screening has proven to be one of the effective tools for diagnosis of breast cancer. Computer aided diagnosis (CAD) system is a fast, reliable, and cost-effective tool in assisting the radiologists/physicians for diagnosis of breast cancer. CAD systems play an increasingly important role in the clinics by providing a second opinion. Clinical trials have shown that CAD systems have improved the accuracy of breast cancer detection. A typical CAD system involves three major steps i.e. segmentation of suspected lesions, feature extraction and classification of these regions into normal or abnormal class and further into benign or malignant stages. The diagnostics ability of any CAD system is dependent on accurate segmentation, feature extraction techniques and most importantly classification tools that have ability to discriminate the normal tissues from the abnormal tissues. In this chapter we discuss the application of machine learning algorithms e.g. ANN, binary tree, SVM, etc. together with segmentation and feature extraction techniques in a CAD system development. Various methods used in the detection and diagnosis of breast lesions in mammography are reviewed. A brief introduction of machine learning tools, used in diagnosis and their classification performance on various segmentation and feature extraction techniques is presented.

Breast Cancer Diagnosis With Mammography

2021 ◽  
pp. 107-127
Author(s):  
Abir Baâzaoui ◽  
Walid Barhoumi
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Gold Standard ◽  
Breast Cancer Diagnosis ◽  
Black Box ◽  
Diagnose Breast Cancer ◽  
Cad Systems ◽  
Helpful Tool ◽  
Cancerous Lesion ◽  
Aided Diagnosis

Breast cancer, which is the second-most common and leading cause of cancer death among women, has witnessed growing interest in the two last decades. Fortunately, its early detection is the most effective way to detect and diagnose breast cancer. Although mammography is the gold standard for screening, its difficult interpretation leads to an increase in missed cancers and misinterpreted non-cancerous lesion rates. Therefore, computer-aided diagnosis (CAD) systems can be a great helpful tool for assisting radiologists in mammogram interpretation. Nonetheless, these systems are limited by their black-box outputs, which decreases the radiologists' confidence. To circumvent this limit, content-based mammogram retrieval (CBMR) is used as an alternative to traditional CAD systems. Herein, authors systematically review the state-of-the-art on mammography-based breast cancer CAD methods, while focusing on recent advances in CBMR methods. In order to have a complete review, mammography imaging principles and its correlation with breast anatomy are also discussed.

scholarly journals Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

2019 ◽  
Vol 9 (15) ◽  
pp. 3130 ◽  
Author(s):  
Navarro ◽  
Perez
Keyword(s):  
Feature Extraction ◽  
Pattern Classification ◽  
Texture Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Post Processing ◽  
Image Partition ◽  
Global And Local ◽  
Bagging Ensemble ◽  
Color Texture

Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.

scholarly journals Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Seyed Reza Kamel ◽  
Reyhaneh YaghoubZadeh ◽  
Maryam Kheirabadi
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Support Vector Machine ◽  
Feature Selection Method ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Gray Wolf ◽  
Diagnose Breast Cancer ◽  
Computer Methods ◽  
A New Technique

Abstract One of the most common diseases among women is breast cancer, the early diagnosis of which is of paramount importance. Given the time-consuming nature of the diagnosis process of the disease, using new methods such as computer science is extremely important for early detection of the condition. Today, the main emphasis is on the science of data mining as one of the computer methods in the field of diagnosis. In the present study, we used data mining as a combination of feature selection method by Gray Wolf Optimization (GWO) and support vector machine (SVM), which is a new technique with high accuracy compared to other methods in this classification, to increase the accuracy of breast cancer diagnosis. The UCI dataset and functional parameters and various statistical criteria were applied to evaluate the proposed method and assess the validity of the results in MATLAB, respectively. Application of the proposed method increased the improvement of the evaluated criteria, which increased the accuracy of diagnosis by 27.68%, compared to former works in the field. As such, it could be concluded that the proposed method had a higher ability to diagnose breast cancer, compared to previous techniques.

Keyboard Characters Recognition with Support Vector Machine

2013 ◽  
Vol 336-338 ◽  
pp. 2283-2287
Author(s):  
Xin Wen Gao ◽  
Xing Jian Guan ◽  
Ben Bo Guan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Dimensional Space ◽  
Training Data ◽  
Support Vector ◽  
Inspection System ◽  
Higher Dimensional ◽  
Key Technologies ◽  
Recognition Function ◽  
The Difference

This paper proposed a method to detect the defects of keyboard characters. The work, which is a part of the keyboard inspection system, integrates two key technologies to realize the recognition function. First, Feature extraction is applied to select the best properties of the keyboard characters to distinguish the difference and six features are chosen. Second, we integrate support vector machine (SVM) into the classification method and the radial basis kernel function is used to map the training data into higher dimensional space to facilitate the classification. We get a satisfied result in the classification finally which demonstrate the proposed approach is effective.

IMPLEMENTATION OF SUPPORT VECTOR MACHINES FOR CLASSIFICATION OF CLINICAL DATASETS

2010 ◽  
Vol 07 (04) ◽  
pp. 347-356
Author(s):  
E. SIVASANKAR ◽  
R. S. RAJESH
Keyword(s):  
Feature Extraction ◽  
Dimensional Space ◽  
Principal Component ◽  
Kernel Functions ◽  
Support Vector ◽  
Optimal Hyperplane ◽  
Higher Dimensional ◽  
Vector Machines ◽  
Map Data

In this paper, Principal Component Analysis is used for feature extraction, and a statistical learning based Support Vector Machine is designed for functional classification of clinical data. Appendicitis data collected from BHEL Hospital, Trichy is taken and classified under three classes. Feature extraction transforms the data in the high-dimensional space to a space of fewer dimensions. The classification is done by constructing an optimal hyperplane that separates the members from the nonmembers of the class. For linearly nonseparable data, Kernel functions are used to map data to a higher dimensional space and there the optimal hyperplane is found. This paper works with different SVMs based on radial basis and polynomial kernels, and their performances are compared.

scholarly journals An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3903 ◽  
Author(s):  
Chiranji Lal Chowdhary ◽  
Mohit Mittal ◽  
Kumaresan P. ◽  
P. A. Pattanaik ◽  
Zbigniew Marszalek
Keyword(s):  
Breast Cancer ◽  
Classification System ◽  
Medical Images ◽  
Research Work ◽  
Support Vector ◽  
Clustering Methods ◽  
Analysis Society ◽  
Fuzzy Clustering Methods ◽  
Detection And Diagnosis

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

Digital Image Technology Mineral Feature Extraction Method Research

2014 ◽  
Vol 989-994 ◽  
pp. 3906-3909
Author(s):  
Jian Peng ◽  
Dong Bo Li
Keyword(s):  
Feature Extraction ◽  
Traditional Method ◽  
Extraction Method ◽  
Texture Classification ◽  
Gabor Wavelet ◽  
Gabor Transform ◽  
Support Vector ◽  
Gray Level ◽  
Feature Extraction Method ◽  
Occurrence Matrix

This paper presents a texture classification algorithm using Gabor wavelet and Gray Level Co-occurrence Matrix as feature extraction method and Support Vector Machine as classifier. Gabor transform and Gray Level Co-occurrence Matrix are used to get the features of the digital images, SVM classifiers are followed to build image and realize classification. The results of the experiments have shown that the methods described in this paper can improve the rate of correct classification effectively than traditional method of classification.

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