Breast Cancer Prediction Based on Mammographic data by Hybrid Resnet and Decision Tree
G. Jayandhi, J.S. Leena Jasmine, R. Seetharaman, S. Mary Joans · 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · 2022
Recently, Breast cancer is easily occurred in all women due to poor eating habits. Further, technological improvement also leads to the causes of breast cancer. Image processing-based solution is an effective solution for identifying breast cancer with a higher rate of accuracy. Compared to other algorithms deep learning based detection methods gives a promising solution in the medical field. In this work, the Deep Residual learning model is joined with a Decision Tree Machine Learning strategy for an efficient breast cancer forecast. Therefore proposed work is named as Resnet-Decision Tree (RDT) model. This RDT model is utilized for anticipating the likelihood of cancer on the server side. The RDT model is created and verified in terms of Recall, accuracy, accuracy, and specificity separately. The outcome showed that the HDL model has higher expectation accuracy than the conventional strategies