Magnetic Resonance Images Based Cervical Cancer Classification Using Convolutional Neural Network
| dc.contributor.advisor | Dawit Assefa (PhD) | |
| dc.contributor.author | Kidist Kebede | |
| dc.date.accessioned | 2025-05-14T09:33:04Z | |
| dc.date.available | 2025-05-14T09:33:04Z | |
| dc.date.issued | 2022-06 | |
| dc.description.abstract | Cervical cancer is one type of cancer which affects the cells of the lower parts of the uterus that connects to the vagina called cervix. According to statistics from the ICO/IARC HPV information center, the yearly estimate of cervical cancer in 2020 was 604, 127 new cases and 341, 831 deaths globally, and the second most common in developing countries. It is also reported that more than 85% of cervical cancer patients are living in low resource setting countries being a major cause of morbidity and mortality. To reduce the death rate, effective screening, diagnosis, staging and treatment should be available. The current thesis study aims to bring automatic staging scheme to be used as a decision support system for cervical cancer treatment and prognosis. Pelvic Magnetic Resonance Images were acquired from St. Paul Hospital Medical Millennium College and Tikur Anbessa Specialized Hospital. Basic image pre-processing was employed on the acquired input images. Then two dimensional convolutional neural network was utilized as an integrated feature extraction and classification scheme. The effect of Network layer variations and important network hyper parameters, including learning rate, number of filters, kernel size and epoch was investigated. The performance of the proposed algorithm in binary (two class) and multi (three and five class) classification were tested and resulted in best classification accuracy of 85%, 68.8% and 56.9% respectively. CNN performance was also compared against two other machine learning approaches, namely Support Vector Machine and K Nearest Neighborhood, where both employed region descriptors as well as Gray level co-occurrence matrix during feature extraction. Results showed that the proposed Neural Network based classification scheme outperforms the two machine learning approaches showing its great promises to assist physicians as a decision support system. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/5464 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Cervical Cancer | |
| dc.subject | Convolutional Neural Network | |
| dc.subject | Magnetic Resonance Image | |
| dc.subject | Staging | |
| dc.subject | Classification. | |
| dc.title | Magnetic Resonance Images Based Cervical Cancer Classification Using Convolutional Neural Network | |
| dc.type | Thesis |