Proceedings ArticleDOI
Automatic detection of tumor subtype in mammograms based On GLCM and DWT features using SVM
M. Mohamed Fathima,D. Manimegalai,S. Thaiyalnayaki +2 more
- pp 809-813
TLDR
The proposed work increases the accuracy of classification and reduces the percentage of false positives in mammography images, since they are most effective, low cost and one of the highly sensitive techniques.Abstract:
Mammography images are employed in diagnosing breast cancers, since they are most effective, low cost and one of the highly sensitive techniques such that they can detect even small lesions. The proposed work increases the accuracy of classification and reduces the percentage of false positives. The images from the data set are initially preprocessed and contrast enhanced which makes the image most effective for further analysis. Then Region Of Interest (ROI) is determined from morphological top hat filtered image by means of thresholding segmentation. Various features like first order textural features, Gray Level Co-occurrence Matrix (GLCM) features, Discrete Wavelet Transform (DWT) features, run length features and higher order gradient features are derived for the particular ROI. Support Vector Machine (SVM) classifier is trained with the above mentioned features using MATLAB bioinformatics tool box. Thus the classified results are obtained for the query image based on the trained SVM structure. The mammography data set has been taken from the Mammographic Image Analysis Society (MIAS) in which there are 322 images available along with ground tooth information.read more
Citations
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An Efficient Image Processing Technique To Automatically Detect Microcalcifications in Mammograms of Breast Cancer
TL;DR: The Approach can be very useful to Radiologist to detect Microcalcifications and gives good Result and is presented in this paper.
Journal ArticleDOI
An Efficient Galactic Swarm Optimization Based Fractal Neural Network Model with DWT for Malignant Melanoma Prediction
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Cancer Mammography Detection Using Four Features Extractions on Gray Level Co-occurrence Matrix with SVM Kernel Analysis
TL;DR: In this article , the authors used a combination of Gray Level Co-occurrence Matrix (GLCM) with a distance equal to 1 and angle direction (0°, 45°, 90°, 135°).
Hybrid Clustering Scheme for the Classification of Lesions in Mammogram Images.
TL;DR: An efficient approach to search for global threshold of image using Gaussian mixture model is proposed and a fuzzy-neural classifier is used for the classification of the mammogram images in to benignant and malignant tissues.
Proceedings ArticleDOI
Breast Cancer Prognosis using Machine Learning Techniques: A Literature Survey
TL;DR: In this article , the authors compared five machine learning algorithms: decision trees (C4.5), support vector machines (SVMs), logistic regression (LR), random forests, and convolutional neural networks (CNNs).
References
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A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques
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Journal ArticleDOI
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Anna Karahaliou,I. Boniatis,Spyros Skiadopoulos,Filippos Sakellaropoulos,Nikolaos Arikidis,E. Likaki,G.S. Panayiotakis,Lena Costaridou +7 more
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Mammogram segmentation by contour searching and massive lesion classification with neural network
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Proceedings Article
Mammography classification by an association rule-based classifier
TL;DR: This paper illustrates, by comparison to other published research, how important the data cleaning phase is in building an accurate data mining architecture for image classification.
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