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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...
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...
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...
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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