FA -WSI -CNN Model for Predicting Breast Cancer using Deep Learning

Abstract

Deep Learning is used for predicting a large volume of data sets in the medical field particularly for breast cancer prediction and diagnosis. The most effective and broadly applied model for detecting breast cancer is the Conventional Neural Network (CNN) among the various deep learning algorithms available. The existing CNN models are lacking in the analysis of a fully labeled Whole Set Image (WSI) data set. The proposed Fully Automate WSI with the CNN model will analyze the whole slide images and patch the input image for improving the accuracy. Then CNN model will get input from patched images and creates classified data for predicting breast cancer. The scikit-learn deep learning framework with Python is used to analyze the result and build a generalized tissue classifier, the WSI data set should include tissues generated under numerous different preparation circumstances. The proposed model experimental results shows promising WSI patch values, accuracy, precision, re-call, and F1 score of the breast cancer tissues which are used for diagnosis purposes. The FA -WSI -CNN model can reduce the training time by evaluating the inference time