Breast Cancer Classification and Proof of Key Artificial Neural Network Terminologies

Classification is one of the interesting areas in the academic field of Neural Networks. Artificial Neural Networks (ANNs) have been extensively used in pattern recognition and classification of data in the supervised and unsupervised environment. The ANNs use advanced concepts of computer science where a machine mimics human intelligence while learning from possible experience. To make a machine self-adaptive and autonomous, the machine is properly trained on a training data-set and then subsequently tested on new data. The excellent quality of training of ANNs typically depends on the underlying architecture of the network they employ, for a specific instance, a considerable number of deep layers, number of key nodes in each distinct layer, epoch size, and activation function. In this academic paper, the practical importance of these architectural components is carefully investigated. This paper is precisely about providing a solution that how ANNs can help us in Breast Cancer Classification. Furthermore, sufficient proofs of some extremely important terminologies used in ANNs are also discussed which will clarify the important concepts of ANNs.