A Novel Approach For Breast Cancer Detection Using Multi-Phase Machine Learning Techniques

Abstract

Breast cancer has risen to prominence as one of the leading causes of death during the last ten years.  Researchers in the field of cancer have developed algorithms that are capable of detecting Breast cancer disease in its early stages using a number of machine learning approaches. This work proposes and evaluates a novel brain MRI-based extraction method. This suggested research offers a unique method for diagnosing Breast cancer using MRI images.  We used multi-phase feature selection approaches to improve performance. In order to pick more ideal features, we segmented the images using FBSO feature selection approach and defined three standard datasets S1, S2, and S3 and analysed them using deep learning and machine learning methods. In our study, we achieved 98.9% classifier optimality and 96.7% accuracy. In compared to earlier studies, the novel method showed the greatest level of reliability and had the most efficient classifier system