Detecting stages of cervical cancer using Machine Learning Techniques (Record no. 6916)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 00963nam a22001457a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 221020b ||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--AUTHOR | |
| Author | OLUWADARE, Samuel Ibukunoluwa |
| 245 ## - TITLE STATEMENT | |
| Title | Detecting stages of cervical cancer using Machine Learning Techniques |
| 250 ## - SUPERVISOR | |
| Supervisor | Dr. Deborah Aleburu |
| 260 ## - IMPRINT | |
| Place of publication | Ibafo |
| Department (College) | Computer Science |
| Date of publication | 2022 |
| 300 ## - COLLATION | |
| Pagination | ix; 86pgs. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Around the world, cancer is known as one of the deadliest diseases in existence, usually patients diagnosed with these diseases perceive it as a death sentence, contrary to their worries, cancer is treatable, preferably in its early stages. Cervical cancer is known as the development of abnormal cells around the cervix, this cancer occurs only in women. When diagnosed with cervical cancer, it is important to convey that if diagnosed in its early stages, cervical cancer can be cured and treatment is an option. |
| 650 ## - TRACINGS | |
| Main Subject | Applied Science |
| Subdivision (1st) | Computer science |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Item type | Students Thesis |
| Source of classification or shelving scheme | Not for loan | Permanent location | Current location | Date acquired | Accen. No. | Copy number | Koha item type |
|---|---|---|---|---|---|---|---|
| Main Library | Main Library | 20.10.2022 | 18010301025 | 1 | Students Thesis |