Breast Cancer Detection Using Image Processing and Machine Learning

• Main Authors: Akshaya, A, Manjula Sanjay, Koti, Priyadarshini, S.
• Format: Article
• Language: English; English
• Published: INTI International University 2024
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; RC0254 Neoplasms. Tumors. Oncology (including Cancer); T Technology (General)
• Online Access: http://eprints.intimal.edu.my/2079/1/joit2024_34.pdf; http://eprints.intimal.edu.my/2079/2/620; http://eprints.intimal.edu.my/2079/; http://ipublishing.intimal.edu.my/joint.html

As the outlines picturize, one driving reason for death in women across the entire world is breast cancer. It is an often-occurring disease in women, affecting approximately 2.1 million women annually. Studies indicate it generally affects women more in developed regions, although rates are increasing globally. While prevention may not be a feasible option, improving the outcomes and survival rates of breast cancer is a viable goal. Breast cancer mortality can be considerably decreased by more efficient treatments, which are made possible by early discovery of the disease. Many researchers and scientists are working on methods to facilitate early detection of breast cancer. Using the K-Nearest Neighbors (KNN) algorithm is one such technique. KNN is a straightforward machine learning technique that works well for regression and classification. In order to categorize an input according to the majority class of its neighbors, it first finds the k-nearest data points to the input. Using features taken from medical imaging, KNN can be utilized to determine a tumor's malignancy or benignity in the context of breast cancer detection. This algorithm is a useful tool for creating precise and dependable diagnostic systems since it can adjust and get better with additional data.