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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
|
| Subjects: | |
| 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 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|