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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Journal ArticleDOI
A novel classification scheme to decline the mortality rate among women due to breast tumor.
TL;DR: A robust classification model for automated diagnosis of the breast tumor with reduction of false assumptions in medical informatics is presented and it is observed that rate of false positives decreased by the proposed method to improve the performance of classification, efficiently.
Proceedings ArticleDOI
A deep learning-based segmentation method for brain tumor in MR images
TL;DR: A novel and new coarse-to-fine method is proposed to segment the brain tumor using a hierarchical framework that consists of preprocessing, deep learning network based classification and post-processing.
Comparison Classifier: Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) In Digital Mammogram Images
TL;DR: In classification process, this research attempt to compare k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifier in order to achieve the better accuracy, and shows that SVM outperforms KNN in breast cancer abnormalities classification with 93.88% accuracy.
Journal ArticleDOI
A new approach for the detection of mammary calcifications by using the white Top-Hat transform and thresholding of Otsu
TL;DR: A system for the detection of calcifications, based on a new approach suggested of pretreatment of image mammography, is proposed, which allows extracting successfully the calcifications starting from the mammography referents from the mini-MIAS database.
Journal ArticleDOI
Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images.
TL;DR: A new algorithm to detect the pattern of a microcalcification by calculating its physical characteristics is proposed, which acts as a good classifier that is used to classify the considered input image as normal or abnormal with the help of only two physical characteristics.
References
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Journal ArticleDOI
Toward breast cancer diagnosis based on automated segmentation of masses in mammograms
TL;DR: It was concluded that features extracted from automated contours can contribute to the diagnosis of breast masses in screening programs by correctly identifying a majority of benign masses.
Journal ArticleDOI
Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer
TL;DR: A novel soft clustered based direct learning classifier which creates soft clusters within a class and learns using direct calculation of weights and applies them to form sub-classes within benign and malignant classes.
Journal ArticleDOI
A GAs based approach for mining breast cancer pattern
Ta-Cheng Chen,Tung-Chou Hsu +1 more
TL;DR: A genetic algorithms (GAs) based approach to assess breast cancer pattern is proposed for extracting the decision rules including the predictors, the corresponding inequality and threshold values simultaneously so as to building a decision-making model with maximum prediction accuracy.
Journal ArticleDOI
Optimizing Case-Based Detection Performance in a Multiview CAD System for Mammography
TL;DR: A new learning method for multiview CAD systems, which is aimed at optimizing case-based detection performance, is proposed, which builds on a single-view lesion detection system and a correspondence classifier.
Journal ArticleDOI
Associative classification of mammograms using weighted rules
TL;DR: The experimental results show that this method works well for such datasets, incurring accuracies as high as 89%, which surpasses the accuracy rates of other rule based classification techniques.
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