Enhanced Deep Convolutional Neural Network for Breast Cancer Recurrence Prognosis

Abstract

As the most common illness affecting women, breast cancer is thought to be diagnosed in about 2.1 million new cases annually. Nearly 30% of individuals who had early-stage cancer treatment had a recurrence within ten years. One important feature of breast cancer behavior that is closely associated with death is recurrence. The fact that a sizable fraction of breast cancer databases seldom contains it, despite its significance, complicates study into its prediction. It is challenging to anticipate who will have a recurrence and who won't, which has consequences for the associated therapy. If artificial intelligence (AI) techniques are created that can predict the probability of a breast cancer recurrence, then clinicians treating the disease may be able to prevent unnecessary overtreatment. This study presents a unique deep convolutional neural network (DCNN) algorithm-based automated system for classifying and predicting breast cancer recurrences.