Enhanced Deep Convolutional Neural Network for Breast Cancer Recurrence Prognosis
K. Manikandan, R. Deiva Nayagam, Arpit Namdev, K. Sudha, Trupti Patil, Ankur Gupta, Sabyasachi Pramanik · Advances in healthcare information systems and administration book series · 2024
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.