Open Access
Early Detection of Breast Cancer using SVM Classifier Technique
Y. Ireaneus Anna Rejani,S. Thamarai Selvi +1 more
- Vol. 3, pp 127-130
TLDR
In this paper, a tumor detection algorithm from mammogram is presented, where the mammogram enhancement procedure includes filtering, top hat operation, DWT, and contrast stretching is used to increase the contrast of the image.Abstract:
This paper presents a tumor detection algorithm from mammogram. The proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract features which categorize tumors. The tumor detection method follows the scheme of (a) mammogram enhancement. (b) The segmentation of the tumor area. (c) The extraction of features from the segmented tumor area. (d) The use of SVM classifier. The enhancement can be defined as conversion of the image quality to a better and more understandable level. The mammogram enhancement procedure includes filtering, top hat operation, DWT. Then the contrast stretching is used to increase the contrast of the image. The segmentation of mammogram images has been playing an important role to improve the detection and diagnosis of breast cancer. The most common segmentation method used is thresholding. The features are extracted from the segmented breast area. Next stage include, which classifies the regions using the SVM classifier. The method was tested on 75 mammographic images, from the mini-MIAS database. The methodology achieved a sensitivity of 88.75%.read more
Citations
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Journal ArticleDOI
Machine learning in manufacturing: advantages, challenges, and applications
TL;DR: In this article, the authors present an overview of available machine learning techniques and structuring this rather complicated area, and a special focus is laid on the potential benefit and examples of successful applications in a manufacturing environment.
Journal ArticleDOI
Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition
Zhennan Yan,Yiqiang Zhan,Zhigang Peng,Shu Liao,Yoshihisa Shinagawa,Shaoting Zhang,Dimitris N. Metaxas,Xiang Sean Zhou +7 more
TL;DR: A multi-stage deep learning framework for image classification and apply it on bodypart recognition achieves better performances than state-of-the-art approaches, including the standard deep CNN.
Journal ArticleDOI
Breast Cancer Diagnosis by using k-Nearest Neighbor with Different Distances and Classification Rules
TL;DR: This work studies and evaluates the performance of different distances that can be used in the K-NN algorithm and analyzes this distance by using different values of the parameter “k” and by using several rules of classification.
Journal ArticleDOI
A Survey on Artificial Intelligence Approaches for Medical Image Classification
S. N. Deepa,B. Aruna Devi +1 more
TL;DR: This study gathers representative works that exhibit how AI is applied to the solution of very different problems related to different diagnostic science analysis, and detects the methods of artificial intelligence that are used frequently together to solve the special problems of medicine.
Journal ArticleDOI
Application of wavelet techniques for cancer diagnosis using ultrasound images
Vidya K. Sudarshan,Muthu Rama Krishnan Mookiah,U. Rajendra Acharya,Vinod Chandran,Filippo Molinari,Hamido Fujita,Kwan Hoong Ng +6 more
TL;DR: Wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images using supervised classifiers are reviewed.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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Digital Image Processing Using MATLAB
TL;DR: 1. Fundamentals of Image Processing, 2. Intensity Transformations and Spatial Filtering, and 3. Frequency Domain Processing.
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TL;DR: How the abstract learning theory established conditions for generalization which are more general than those discussed in classical statistical paradigms are demonstrated and how the understanding of these conditions inspired new algorithmic approaches to function estimation problems are demonstrated.
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Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center.
TL;DR: The use of CAD in the interpretation of screening mammograms can increase the detection of early-stage malignancies without undue effect on the recall rate or positive predictive value for biopsy.
Journal ArticleDOI
The connection between regularization operators and support vector kernels
TL;DR: It is proved that the Green's Functions associated with regularization operators are suitable support vector kernels with equivalent regularization properties and it is shown that a large number of radial basis functions, namely conditionally positive definite functions, may be used as support vector kernel.