Proceedings ArticleDOI
Detection And Classification Technique Of Breast Cancer Using Multi Kernal SVM Classifier Approach
TLDR
This techniques shows how easily the authors can detect region of tumor is present in mammogram images with more than 80% of accuracy rates for linear classification using SVM.Abstract:
Today, one of the mostly seen disease in women is Breast Cancer. It is circulated different countries of all over the world. Mammography is a kind of low-powered X-ray diagnosis approach for detection and diagnosis of cancer diseases early. This task is done for classification of diseases as Malignant or Benign. The entire work focuses on the basis of two cases. One is detection of different type of tumors as suspicious regions and another is process to extract features from mammogram images and classification of type of tumors presented. There are some phases of detection of tumour: image pre-processing, image enhancement using histogram, extraction of features from mammographic images, Segmentation using Otsu thresholding method, classification using Support Vector Machine (SVM) classifier. Image Preprocessing is basically done by applying two dimensional median filter and histogram equalization for getting more enhanced image. Then extraction of features set is performed from the images. Here the different types of tumor like Benign, Malignant, or Normal image are classified using the SVM classifier. In this technique, we have used statistical parameter like as entropy, mean, RMS, correlation, variance, standard deviation. This techniques shows how easily we can detect region of tumor is present in mammogram images with more than 80% of accuracy rates for linear classification using SVM.read more
Citations
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Prostate Cancer Prognosis-a comparative approach using Machine Learning Techniques
Sagar.C. Bellad,Ananya Mahapatra,Sahil Dilip Ghule,Satvik Sridhar Shetty,S. Sountharrajan,M. Karthiga,E. Suganya +6 more
TL;DR: In this article, the authors focused on the working of various classifiers for prediction of prostate Cancer in calculating the level of efficiency in prediction and this helps in selecting the best method.
References
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Posted Content
Early Detection of Breast Cancer using SVM Classifier Technique
TL;DR: A tumor detection algorithm from mammogram that focuses on the solution of two problems, how to detect tumors as suspicious regions with a very weak contrast to their background and how to extract features which categorize tumors.
Journal ArticleDOI
Model Selection for Small Sample Regression
TL;DR: This work presents a new penalization method for performing model selection for regression that is appropriate even for small samples, based on an accurate estimator of the ratio of the expected training error and the expected generalization error, in terms of theexpected eigenvalues of the input covariance matrix.
Early Detection of Breast Cancer using SVM Classifier Technique
TL;DR: 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.
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