REVOLUTIONIZING BREAST CANCER MANAGEMENT: HARNESSING THE POWER OF MACHINE LEARNING IN DIAGNOSIS AND TREATMENT

Abstract

Breast cancer is a worldwide health issue that demands precise and efficient management. By recognizing patterns and links in massive datasets and establishing personalized treatment plans, machine learning has shown promise for advancing breast cancer care. Early diagnosis of tumor can dramatically improve patients' prognoses and chances of survival by promoting timely therapeutic therapy. More accurate categorization of benign tumours may protect patients from needless therapies. As a result, substantial study is being conducted into the precise analysis of Breast Cancer and the organization of those diagnosed into malignant or benign types. However, there are several issues with machine learning, such as accessibility and quality, ethical concerns, and accountability. The purpose of this thesis is to present a complete assessment of the present state of advances in machine learning in breast cancer management as well as identify potential and obstacles connected with their incorporation into clinical practice. This thesis will add to the current attempts to revolutionize breast cancer treatment through the potential of machine learning by studying the most recent research and breakthroughs in this sector.