This document discusses using deep learning to predict breast cancer based on tumor data. It proposes using a neural network model to classify tumors as malignant or benign. The key steps are: 1. Collecting and preprocessing tumor cell data to remove noise and inconsistencies. 2. Developing a neural network model and training it on labeled training data to learn patterns. 3. Testing the trained model on unlabeled testing data to evaluate its accuracy in classifying tumors. The goal is to develop an accurate model to help doctors determine the critical condition of patients and classify difficult tumors.