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Ensemble classifier for early detection of breast cancer
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| dc.contributor.author | ISHIMWE, Beza Aime | |
| dc.date.accessioned | 2025-09-09T09:28:04Z | |
| dc.date.available | 2025-09-09T09:28:04Z | |
| dc.date.issued | 2023-02-08 | |
| dc.identifier.uri | http://dr.ur.ac.rw/handle/123456789/2422 | |
| dc.description | Master's Dissertation | en_US |
| dc.description.abstract | The first chapter provides an outline of how breast cancer has evolved into a global issue. It also depicts the various approaches that have been utilized and are currently being used to address the problem of breast cancer,which primarily affects women. We conducted this research in light of the aforementioned difficulties. We are employing several machine learning algorithms to raise the patient’s breast cancer awareness in order to discover a systematic strategy to address breast cancer. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | University of Rwanda (College of science and Technology) | en_US |
| dc.publisher | University of Rwanda (College of science and Technology) | en_US |
| dc.subject | Breast patients | en_US |
| dc.subject | Breast cancer | en_US |
| dc.subject | Mammogram images | en_US |
| dc.title | Ensemble classifier for early detection of breast cancer | en_US |
| dc.type | Dissertation | en_US |
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