Neural Network Aided Breast Cancer Detection and Diagnosis Using Support Vector Machine

An artificial neural network (ANN) is an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the mammalian brain processes information. The key element of the ANN paradigm is the novel structure of the information processing system. Learning in ANN typically occurs by example through training, or exposure to a set of input/output data where the training algorithm iteratively adjusts the connection weights (synapses). These connection weights store the knowledge necessary to solve specific problems. In this work, we have used neural networks Support Vector Machine method for diagnosis of breast cancer. SVMs can only be used for classification, not for function approximation. The Support Vector Machine (SVM) is implemented using the kernel Adatron algorithm. The kernel Adatron maps inputs to a high-dimensional feature space, and then optimally separates data into their respective classes by isolating those inputs, which fall close to the data boundaries.The proposed neural network model hold promise for radiologists, surgeons, and patients with information, which was previously available only through biopsy, thus substantially reducing the number of unnecessary surgical procedures. For training and testing the neural network various databases available on the Internet as well as gathered information from hospitals is used.