Breast Cancer Detection Using Image Processing and Machine Learning

Akshaya, A and Manjula Sanjay, Koti and Priyadarshini, S. (2024) Breast Cancer Detection Using Image Processing and Machine Learning. Journal of Innovation and Technology, 2024 (34). pp. 1-7. ISSN 2805-5179

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Benign and malignant, Cancer Detection, Image Processing, K-NN
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology > T Technology (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	04 Dec 2024 07:53
Last Modified:	04 Dec 2024 07:53
URI:	http://eprints.intimal.edu.my/id/eprint/2079

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