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Three-dimensional imaging based on radio frequency that exploits the contrast in dielectric properties of tissues may be used as a low-cost, non-invasive and non-ionizing methodology for breast cancer detection. This paper demonstrates the use of various supervised machine learning algorithms in classification of breast tissues into less-dense fatty and dense fibroglandular or malignant classes from...
Breast cancer has caused more and more attention in recent years since the mortality rate is increasing and age of onset is trend to be younger than before. Using computer vision technology for automatic classifying benign and masses malignant ones could assist doctors in diagnosing condition. However, the margins and shapes of masses are various and which are very similar with surrounding tissues,...
Cancer prognosis prediction improves the quality of treatment and increases the survivability of the patients. Conventional methods of cancer prediction deal with single class by limiting the prognosis prediction to one response variable. The SEER Public Use cancer database has more prominent variables that support better prediction approach. The objective of this paper is to find the prominent labels...
Intelligent Computer Aided Diagnosis (CAD) Systems can be used for detecting Microcalcification (MC) clusters in digital mammograms at the early stage. CAD systems help radiologists in identifying tumor patterns in an efficient and faster manner than other detection methods. In this paper, we propose a new approach for detecting tumors in mammograms using Radial Basis Function Networks (RBFNN). Prior...
Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The main...
The “black box” problem in neural network is being much concerned, which contributes to more and more researches on the structures of the neural network. Hierarchical neural network (HNN) is one kind of the neural networks that pays attention to the inner structure of network with the presentation of modular parts. In order to reducing the dependence of expert system in HNN, in the paper, a data attributes...
This paper analyzes the application of different classification techniques for Electroencephalography (EEG) signals. Fuzzy Functions Support Vector Classifier (FFSVC), Improved Fuzzy Functions Support Vector Classifier (IFFSVC) and a novel hybrid technique that has been designed utilizing Particle Swarm Optimization and Radial Basis Function Networks (PSO-RBFN) have been studied. The classification...
The classification of breast masses into benign and malignant categories plays an important role in the area of computer-aided diagnosis (CAD) of breast cancer. In this paper, one novel scheme based on multi-view information fusion is proposed, in order to improve the accuracy and the robustness of the classification and reduce the false positive rates. Five contour and shape features of the masses...
The classification of breast masses into benign and malignant categories plays an important role in the area of computer-aided diagnosis (CAD) of breast cancer. In this paper, in order to improve the accuracy and the robustness of the classification and reduce the false positive rates, we proposed one novel scheme that was based on information fusion in multi views. A series of contour and shape features...
The presence of metastasis in the regional lymph nodes is the most important factor in predicting prognosis in breast cancer. Many biomarkers have been identified that appear to relate to the aggressive behaviour of cancer. However, the nonlinear relation of these markers to nodal status and also the existence of complex interaction between markers has prohibited an accurate prognosis. The aim of...
Mammography is the most effective and available tool for breast cancer screening. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Data mining algorithms could be used to help physicians in their decisions to perform a breast biopsy on a suspicious lesion seen in a mammogram image...
A major challenge in microarray classification and biomarker discovery is dealing with small-sample high-dimensional data where the number of genes used as features is typically orders of magnitude larger than the number of labeled microarrays. One way to address this challenge is by leveraging information from the publicly accessible repositories of microarray data. Following this idea, a multi-task...
The purpose of this study is to investigate the significance of the multi-agent interactive information fusion algorithm over the matter of identification of breast masses in digitized images. For the lack of enough correlation information between the individual classifiers, the generalization performance of the Bayesian fusion method is sometimes far from the expected level, and thereby the multi-agent...
A experimental scheme for identification of breast diseases based on thermal images is presented. A set of infrared images from a database that is being developed were used for an experimental investigation considering the image Lacunarity measures by the gliding box algorithm. This approach generates a parameter to distinguish from normal to abnormal breast diagnostics. We propose two interpretations...
Breast cancer is the main cause of death for women between the ages of 35 to 55. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. Microcalcifications are among the earliest signs of a breast carcinoma. Actually, as radiologists point out, microcalcifications can be the only mammographic sign of non-palpable breast disease which are often overseen in...
An intelligent computer-aided diagnostics system may be developed to assist the radiologists to recognize the masses/lesions appearing in breast in different groups of benignancy/malignancy. In present work we have attempted to develop a computer assisted treatment planning system implementing Genetic algorithm-based Neuro-fuzzy approaches. The boundary based features of the tumor lesions appearing...
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