A Deep Learning Based Breast Cancer Detection Algorithm with Feature Fusion Techniques
Raval Nikita · Journal of Emerging Technologies and Innovative Research · 2020
Cancer has been portrayed as a heterogeneous disease comprising of a wide range of subtypes. The early diagnosis of a cancer type is very important to determine the course of medical treatment required by the patient. The significance of classification cancerous cells into benign or malignant has driven many research studies, in the biomedical and the bioinformatics field. In the past years researchers have been encouraged to use different machine learning (ML) techniques for cancer detection, as well as prediction of survivability and recurrence. Machine learning with image processing can be used to distinguish key highlights from complex datasets and uncover their significance. The predictive models talks about here depend on different administered ML strategies and on various input features and data samples. We have used genetic algorithm and feature fusion to make the algorithm more efficient. The hybrid algorithm to detect the type of breast cancer (benign or malignant) and selection of features which are more relevant for prediction. We have made a comparative study to find out the best algorithm of the above, for prediction of cancer type. With a high level of accuracy, any of these methods can be used to predict the type of breast cancer of any particular patient.