Breast Cancer Detection Using Image Processing and Machine Learning
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 incre...
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| Format: | Article |
| Language: | English English |
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/2079/ http://eprints.intimal.edu.my/2079/2/620 http://eprints.intimal.edu.my/2079/3/joit2024_34b.pdf |
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| author | Akshaya, A Manjula Sanjay, Koti Priyadarshini, S. |
| author_facet | Akshaya, A Manjula Sanjay, Koti Priyadarshini, S. |
| author_sort | Akshaya, A |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:58:43Z |
| format | Article |
| id | intimal-2079 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:43Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-20792025-07-12T03:00:19Z http://eprints.intimal.edu.my/2079/ Breast Cancer Detection Using Image Processing and Machine Learning Akshaya, A Manjula Sanjay, Koti Priyadarshini, S. QA75 Electronic computers. Computer science QA76 Computer software RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology (General) 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. INTI International University 2024-12 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2079/2/620 text en cc_by_4 http://eprints.intimal.edu.my/2079/3/joit2024_34b.pdf 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 http://ipublishing.intimal.edu.my/joint.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology (General) Akshaya, A Manjula Sanjay, Koti Priyadarshini, S. Breast Cancer Detection Using Image Processing and Machine Learning |
| title | Breast Cancer Detection Using Image Processing and Machine Learning |
| title_full | Breast Cancer Detection Using Image Processing and Machine Learning |
| title_fullStr | Breast Cancer Detection Using Image Processing and Machine Learning |
| title_full_unstemmed | Breast Cancer Detection Using Image Processing and Machine Learning |
| title_short | Breast Cancer Detection Using Image Processing and Machine Learning |
| title_sort | breast cancer detection using image processing and machine learning |
| topic | QA75 Electronic computers. Computer science QA76 Computer software RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology (General) |
| url | http://eprints.intimal.edu.my/2079/ http://eprints.intimal.edu.my/2079/ http://eprints.intimal.edu.my/2079/2/620 http://eprints.intimal.edu.my/2079/3/joit2024_34b.pdf |