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In this paper, we described the practical application of data mining methods for estimation of survival rate and disease relapse for breast cancer patients. A comparative study of prominent machine learning models was carried out and according to the achieved results we concluded that the classifiers obviously learn some of the concepts of breast cancer survivability and recurrence. These algorithms...
Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. Currently there are three techniques to diagnose breast cancer: mammography, FNA (Fine Needle Aspirate) and surgical biopsy. In this paper, we develop a system that can classify “Breast Cancer Disease” tumor using neural network with Feed-forward Backpropagation Algorithm to classify...
In this paper, appropriate and efficient networks for breast cancer knowledge discovery from clinically collected data sets are investigated. Invoking various data mining techniques, it is desired to find out the percentage of disease development, using the developed network. The results, help in choosing a reasonable treatment of the patient. Several neural network structures are evaluated for this...
This paper presents a novel method based on fractal features for the classification of mammogram images. For recognition of regions and objects in the natural scenes, there is always a need for features, which are invariant, and they provide a good set of descriptive values for the region. There are numerous methods available to estimate parameters from the images of the fractal surface. In this paper...
Several structures of artificial neural networks (ANNs) with different training patterns were investigated so as to compare their performances on detecting the cluster of microcalcifications (CM) on mammography. 150 region-of-interests (ROIs) around mass containing both positive and negative microcalcifications were selected for training the network by a standard or modified error-back-propagation...
More analysis has been done to discover the meaningful unusual patterns which may mean fraud or anomaly. In this paper, a two-stage approach considering the labeled data's proposed to discover meaningful unusual observation, without the goal of classifying. We firstly apply hyper surface classification (HSC) algorithm to gain a separating hyper surface which includes several pieces. Observation in...
We present a new curvilinear algorithmic model for training neural networks which is based on a modifications of the memoryless BFGS method that incorporates a curvilinear search. The proposed model exploits the nonconvexity of the error surface based on information provided by the eigensystem of memoryless BFGS matrices using a pair of directions; a memoryless quasi-Newton direction and a direction...
Microarray technology has been widely applied to search for biomarkers of diseases, diagnose diseases and analyze gene regulatory network. Abundance of expression data from microarray experiments are processed by informatics tools, such as supporting vector machines (SVM), artificial neural network (ANN), and so on. These methods achieve good results in single dataset. Nevertheless, most analyses...
Mass detection is one of the main computer-aided mammographic breast cancer detection techniques. Early detection of primary tumor is an essential and effective method to reduce mortality. Computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing abnormalities earlier and faster than traditional screening methods. This paper presents a new approach for detecting...
A major class of problems in medical science involves the diagnosis of a disease based upon various tests performed upon the patient. Cancer is a complex and clinical heterogeneous disease. The research into the diagnosis and treatment of cancer has become an important issue for the scientific community. The objective of cancer classification is to design a classifier to categorize the tissue samples...
This paper aims to review the use of artificial neural networks (ANNs) in prediction of cancer recurrence. The sources of publications were randomly selected from PUBMED database, IEEE explore, and the google search engine with the keywords for searching as ldquorecurrencerdquo or ldquorelapserdquo or ldquodisease freerdquo + ldquoneural networkrdquo + ldquocancerrdquo. Increasing of the predictive...
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