To Detect and Classify Breast Cancer Using Convolution Layer Depth Filter for Prediction of Disease with Feature Validation

Abstract

Globally, the threat of Bio-war cannot be overlooked. Today, there are many popularly known biological diseases and treatments, of which few are dangerous and few are not. And breast cancer is one of them. The study proposes a model to analyze the severity of this problem and find a credible solution to it. Object detection and classification plays a major role for an in-depth analysis into neural network methods. The convolution layer helps in prediction and subsequently the features extracted from this layer are forwarded to feed forward networks. The data set is divided into two ways for the trained set and test set. Filter depth is considered as a measure to feature extraction with utmost quality.  Breast histopathology helps in confirmation of the presence of cancerous cells but it is time consuming.