A Hybridized CNN Approach Augmented With Machine Learning Model For Efficient Segmentation And Classification For Breast Cancer

Abstract

Cancer refers to the uncontrolled multiplication of a group of cells in a particular location of the body. Breast cancer is any form of malignant tumor that develops from breast cells. Breast cancer is among the most persistent malignant growths that can affect women, and it is now one of the leading causes of mortality. The World Health Organization s International Agency for Research on Cancer (IARC) estimates that more than 400,000 women die each year from breast cancer. Today, there is an urgent need for breast cancer control, which is achieved primarily by knowing different risk factors. newlineThe early detection of abnormalities in the breast enables the radiologist to diagnose breast cancer easily. Efficient tools in diagnosing cancerous breasts will help the medical experts provide accurate diagnosis and timely treatment to the patients. Although there are many other kinds of procedures, mammography is one that is usually used to find breast cancer. Mammograms, however, have limited contrast and low image quality. In order to accurately and efficiently distinguish between benign and malignant breast cancer, a feature-based method for image detection of breast cancer is proposed in this study. newlineThe main objectives of breast cancer prediction are to efficiently determine the cancer stage, minimize time consumption, and develop a user-friendly model applicable to diverse datasets. The algorithm should encompass all relevant parameters for accurate predictions, facilitating comprehensive analysis. By prioritizing accuracy, precision, and recall rates, the model aims to achieve highly reliable results, supporting clinicians in making informed decisions for early detection and personalized treatment planning. Through continuous improvement and optimization, the algorithm seeks to provide an efficient and effective tool for breast cancer prediction, ultimately contributing to improved patient outcomes and healthcare practices. newlineIn this research, four different methodologies have been proposed, representing various stage