ARTIFICIAL NEURAL NETWORKS FOR EARLY DETECTION AND DIAGNOSIS OF CANCER

Why use neural networks? The reasons commonly cited in the literature for using artificial neural networks for any problem are many and vari ed. They learn from experience. They work where other algorithms fail. They generalize from the training examples to perform well on indepen dent test data. They reduce the number of false alarms without increas ing significantly the number of false negatives. They are fast and are easier to use than conventional statistical techniques, especially wh en multiple prognostic factors are needed for a given problem. These f actors have been overly promoted for the neural techniques. The common theme of this paper is that artificial neural networks have proven to be an interesting and useful alternate processing strategy. Artificia l neural techniques, however, are not magical solutions with mystical abilities that work without good engineering. With good understanding of their capabilities and limitations they can be applied productively to problems in early detection and diagnosis of cancer. The specific cancer applications which will be used to demonstrate current work in artificial neural networks for cancer detection and diagnosis are brea st cancer, liver cancer and lung cancer.