Characterization of Mammogram Using Ensemble Classification Technique for Detection of Breast Cancer

Breast cancer is one of the most common known cancers in women today. Just like any other form of cancer an early detection of cancer provides better chances of cure. However, it is an arduous task for the radiologists to detect cancer accurately. Thus computer aided diagnosis of the mammographic images is the most popular medium to aid the radiologists in accurately classifying benign and malignant mammographic lesions. In this thesis an efficient approach is presented to classify the mammographic lesion for the detection of breast cancer. In this approach the extracted feature coefficients are balanced using Gaussian distribution. This distribution balances the class unbalanced dataset providing for better classification. This scheme uses Logit Boost classification technique. Logit Boost uses least squared regression cost function on the additive model of Adaboost. The standard MIAS database was used to obtain the mammographic lesions. With a classification accuracy rate of 99.1% and a performance index value of AUC = 0.98 in receiver operating characteristic (ROC) curve the results are pretty much optimal. These results are very promising when compared with existing methods

Analysis of Mammographic Images for Early Detection of Breast Cancer Using Machine Learning Techniques

Breast cancer is the main reason for death among women. Radiographic images obtained from mammography equipment are one of the most frequently used techniques for helping in early detection of breast cancer. The motivation behind this study is to focus the tumour types of breast cancer images .It is methodology to anticipated a sickness in view of the visual conclusion of breast disease tumour types with precision, particularly when numerous feature are related. Breast Cancer (BC) is one such sample where the phenomenon is very complex furthermore numerous feature of tumour types are included. In the present investigation, various pattern recognition techniques were used for the classification of breast cancer using mammograms image processing techniques .The pattern recognition techniques for tumour image enhancements, segmentation, texture based image feature extraction and subsequent classification of breast cancer mammogram image was successfully performed. When two machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) were used to classify 120 images, it was observed from the results that Artificial Neural Network classifiers demonstrated the h classification rate 91.31% and the SVM with both Radial Basis Function (RBF) and linear kernel classifiers demonstrated the highest classification rate of 92.11% and RBF classification rate is 92.85%

Mammographic Ellispe Modelling for Risk Estimation

AbstractIt has been shown that breast density and parenchymal patterns are significant indicators in mammographic risk assessment. In addition, studies have shown that the sensitivity of computer aided tools decreases significantly with increase in breast density. As such, mammographic density estimation and classification plays an important role in CAD systems. In this paper, we present the classification of mammographic images according to breast parenchymal structures through a multi-scale ellipse blob detection technique. Our classification is based on classifying the mammographic images of the MIAS dataset into high/low risk mammograms based on features extracted from a blob detection technique which is based on breast tissue structure. In addition, it evaluates the relation between the BIRADS classes and low/high risk mammograms. Results demonstrate the probability of estimating breast density using computer vision techniques to improve classification of mammographic images as low/high risk

Multi-Classifiers And Decision Fusion For Robust Statistical Pattern Recognition With Applications To Hyperspectral Classification

In this dissertation, a multi-classifier, decision fusion framework is proposed for robust classification of high dimensional data in small-sample-size conditions. Such datasets present two key challenges. (1) The high dimensional feature spaces compromise the classifiers’ generalization ability in that the classifier tends to overit decision boundaries to the training data. This phenomenon is commonly known as the Hughes phenomenon in the pattern classification community. (2) The small-sample-size of the training data results in ill-conditioned estimates of its statistics. Most classifiers rely on accurate estimation of these statistics for modeling training data and labeling test data, and hence ill-conditioned statistical estimates result in poorer classification performance. This dissertation tests the efficacy of the proposed algorithms to classify primarily remotely sensed hyperspectral data and secondarily diagnostic digital mammograms, since these applications naturally result in very high dimensional feature spaces and often do not have sufficiently large training datasets to support the dimensionality of the feature space. Conventional approaches, such as Stepwise LDA (S-LDA) are sub-optimal, in that they utilize a small subset of the rich spectral information provided by hyperspectral data for classification. In contrast, the approach proposed in this dissertation utilizes the entire high dimensional feature space for classification by identifying a suitable partition of this space, employing a bank-of-classifiers to perform “local” classification over this partition, and then merging these local decisions using an appropriate decision fusion mechanism. Adaptive classifier weight assignment and nonlinear pre-processing (in kernel induced spaces) are also proposed within this framework to improve its robustness over a wide range of fidelity conditions. Experimental results demonstrate that the proposed framework results in significant improvements in classification accuracies (as high as a 12% increase) over conventional approaches

Multi-Classifiers And Decision Fusion For Robust Statistical Pattern Recognition With Applications To Hyperspectral Classification

In this dissertation, a multi-classifier, decision fusion framework is proposed for robust classification of high dimensional data in small-sample-size conditions. Such datasets present two key challenges. (1) The high dimensional feature spaces compromise the classifiers’ generalization ability in that the classifier tends to overit decision boundaries to the training data. This phenomenon is commonly known as the Hughes phenomenon in the pattern classification community. (2) The small-sample-size of the training data results in ill-conditioned estimates of its statistics. Most classifiers rely on accurate estimation of these statistics for modeling training data and labeling test data, and hence ill-conditioned statistical estimates result in poorer classification performance. This dissertation tests the efficacy of the proposed algorithms to classify primarily remotely sensed hyperspectral data and secondarily diagnostic digital mammograms, since these applications naturally result in very high dimensional feature spaces and often do not have sufficiently large training datasets to support the dimensionality of the feature space. Conventional approaches, such as Stepwise LDA (S-LDA) are sub-optimal, in that they utilize a small subset of the rich spectral information provided by hyperspectral data for classification. In contrast, the approach proposed in this dissertation utilizes the entire high dimensional feature space for classification by identifying a suitable partition of this space, employing a bank-of-classifiers to perform “local” classification over this partition, and then merging these local decisions using an appropriate decision fusion mechanism. Adaptive classifier weight assignment and nonlinear pre-processing (in kernel induced spaces) are also proposed within this framework to improve its robustness over a wide range of fidelity conditions. Experimental results demonstrate that the proposed framework results in significant improvements in classification accuracies (as high as a 12% increase) over conventional approaches