This paper presents an electronic second opinion system for the classification of mass abnormalities in mammograms into benign and malignant categories. This system is designed to help radiologists to reduce the number of benign breast cancer biopsies. Once a mass abnormality is detected and marked on a mammogram by a radiologist, two textural features, named denseness and architectural distort...

Selecting relevant features in mass spectra analysis is important both for classification and search for causality. In this paper, it is shown how using mutual information can help answering to both objectives, in a model-free nonlinear way. A combination of ranking and forward selection makes it possible to select several feature groups that may lead to similar classification performances, but...

Boosting is a machine learning algorithm that is not well known in chemometrics. We apply boosting tree to the classification of mass spectral data. In the experiment, recognition of 15 chemical substructures from mass spectral data have been taken into account. The performance of boosting is very encouraging. Compared with previous result, boosting significantly improves the accuracy of classi...

This document presents data analysis similar to Wang (2011) using R package bst. Serum samples were collected from 77 benign prostate hyperplasia (BPH), 84 early stage prostate cancer, 83 late stage prostate cancer and 82 age-matched healthy men (HM). Peak detection and alignment were based on surface-enhanced laser desorption/ionization (SELDI) mass spectrometry protein profiles. The data have...

The hierarchical structure in the quark masses and mixings allows its ten physical parameters to be most conveniently encoded in mass matrices of the upper triangular form. We classify these matrices in the hierarchical, minimal parameter basis where the mismatch between the weak and mass eigenstates involves only small mixing angles. Ten such pairs are obtained for the up and down quarks. This...

MOTIVATION Early cancer detection has always been a major research focus in solid tumor oncology. Early tumor detection can theoretically result in lower stage tumors, more treatable diseases and ultimately higher cure rates with less treatment-related morbidities. Protein mass spectrometry is a potentially powerful tool for early cancer detection. We propose a novel method for sample classific...

In this paper, we present a new dimension reduction method using Wilk’s Lambda Average Threshold (WLAT) for classifying the masses present in mammogram. According to Breast Imaging Reporting and Data System (BIRADS) benign and malignant can be differentiated using its shape, size and density, which is how radiologist visualize the mammograms. Measuring regular and irregular shapes mathematicall...

Motivation: Early detection of cancer is crucial for successful treatment, and protein profiling using mass spectrometry (MS) data has been investigated as a potential tool. However, due to the high correlation and huge dimensionality of MS data, it is crucial to modify existing algorithms and to develop new ones, where necessary, for analyzing such data. Results: We develop a group of logistic...

Textural features can be useful in differentiating between benign and malignant breast lesions on mammograms. Unlike previous computerized schemes, which relied largely on shape and margin features based on manual contours of masses, textural features can be determined from regions of interest (ROIs) without precise lesion segmentation. In this study, therefore, we investigated an ROI-based fea...